Climate doi: 10.3390/cli14090184

Authors: Traianos Minos Alkiviadis Stamatakis Dimitrios Kalaronis Evangelia E. Golia

Soil salinization affects more than 424 million hectares of topsoil worldwide and is associated with yield losses of 18–43% in drylands, yet its long-term dynamics have rarely been compared across regions on a common basis. This work synthesizes nine case studies from a systematic, PRISMA-guided search, selected purposively to maximize variation in climate, scale and method. ECe (dS/m) and total salt content (g/kg) have no general conversion, so the eight ECe-based studies are compared class by class using their own figures; the single g/kg study contributes direction only, and no values are pooled. The largest transfers occur between the intermediate and higher classes, while the non-saline class changes little in most settings; intensification of already salt-affected land therefore predominates over conversion of new land, though the latter also occurs (Bachu County). The drivers reported by the source studies are irrigation expansion, inadequate drainage, and shallow saline groundwater, with rising evaporative demand and reduced leaching as amplifiers; since no study partitions the change between climatic and management causes, this ordering is descriptive, not a causal attribution. The synthesis is hypothesis-generating; its main product is a direction-of-change summary table separating observed trends from projections.

Climate doi: 10.3390/cli14090183

Authors: Jimmy Rosales-Huamaní Odón Sánchez-Ccoyllo María Álvarez-Paucar Oscar Toapanta-Cunalata

Accurate estimation of global horizontal irradiance (GHI) is relevant for characterizing solar resources in regions with limited measurement infrastructure. This study compared six machine learning models Polynomial Ridge Regression, Decision Tree, Random Forest, XGBoost, Artificial Neural Network, and K-Nearest Neighbors for the contemporaneous estimation of GHI at a five-minute resolution in an arid coastal climate. After quality control and restriction to daytime periods, 45,024 observations were analyzed using five external chronological blocks with an expanding-window scheme, generating 31,517 out-of-sample estimates. XGBoost achieved the highest R2(0.657±0.192) and the lowest RMSE (108.01 ± 13.67 W m−2), whereas Random Forest yielded lower MAE, WMAPE, and MASE values. DM–HAC sensitivity analysis favored XGBoost under squared-error loss for six of the seven evaluated bandwidths, whereas no significant difference between XGBoost and Random Forest was found under absolute-error loss. None of the models achieved the nominal conformal coverage level of 90%; XGBoost showed the highest empirical coverage and the narrowest prediction intervals (PICP = 0.791; PINAW = 0.312). Predictor-set reduction improved the performance of four of the six algorithms. Overall, XGBoost and Random Forest exhibited complementary performance profiles, indicating that model selection should jointly consider predictive accuracy, temporal stability, predictor sensitivity, and uncertainty.

Climate doi: 10.3390/cli14090182

Authors: Demurtas Valentino Sulis Andrea Azzena Costantino Alemanni Federico Carboni Andrea Luise Giovanni Manconi Veronica Mancosu Gianluigi Sabatini Andrea Santona Giulio Orrù Paolo Emanuele Deiana Giacomo

Mitigating flood risk and planning river corridors in Torrent-type Basins (TBs) requires integrated frameworks that link hydro-geomorphological processes with ecological quality, a combination currently lacking in regional planning. This study presents an integrated hydro-geomorphological and ecological analysis of the Rio Geremeas catchment (Sardinia, Italy), developed within the Regional Sediment Management Plan (PGS). The approach combines multi-scale geomorphological mapping, the IDRAIM eco-morphological framework, and 2D hydro-morphodynamic modelling (MIKE 21C), supported by field surveys and remote sensing of sediment source areas and biological assemblages (riparian vegetation, macroinvertebrates, and fish). Results reveal a direct link between altered sediment dynamics and ecological degradation, with confined reaches showing lower biotic diversity compared to mobile, morphologically functional reaches. 50-year flood simulations identified critical erosion/deposition zones, guiding targeted proposals: restoring sediment continuity by removing hydraulic constraints, controlling invasive species, and managing the river mouth to favour the migration of the European eel. This work provides a transferable, interdisciplinary approach for sediment- and ecosystem-informed river basin planning, directly supporting the EU Water Framework Directive and climate resilience strategies.

Climate doi: 10.3390/cli14090181

Authors: Yifan Wang Ya Tuo Qingquan Li Guolin Feng

The application of sub-seasonal prediction error analysis and interpretation remains a key research frontier of climate prediction. Related studies are essential for deepening the understanding of model performance, investigating error sources, and improving model prediction accuracy. Based on four perspectives—basic characteristics, spatial and temporal consistency, and potential causes of prediction errors—this study systematically investigates the sub-seasonal hindcast errors of summer precipitation over China from the state-of-the-art model system BCC–CPS–S2Sv2 (S2Sv2). The findings reveal that (1) S2Sv2 shows significant prediction skill in the first three pentads and shows an obvious increase in RMSE and a decrease in SCC of the precipitation prediction. This feature is quite similar to other operational models. (2) The prediction error primarily presents typical modes such as the meridional dipole, triple or consistent patterns. Prediction errors maintain spatial consistency in South China, the middle and lower reaches of the Yangtze River, and North China. Temporal consistency of the prediction errors shows a significant correlation between neighboring pentads, which diminishes as the prediction period extends, with a better correlation lasting for 2–4 pentads in some areas. (3) The prediction errors of S2Sv2 are significantly correlated with major circulation patterns in East Asia and key area sea surface temperatures (SSTs). For instance, errors are larger when the West Pacific Subtropical High is stronger, but smaller when the East Asia Trough is stronger. Errors also decrease with higher SSTs in the central equatorial Pacific and increase with higher SSTs in the tropical North Atlantic. This study provides valuable insights into the limitations of the S2Sv2 model and offers important references for error correction and model application.

Climate doi: 10.3390/cli14090180

Authors: Yuki Shiga Rajib Shaw

As the impact of climate change becomes increasingly disruptive worldwide, the gap in adaptation finance is widening. Despite advancements in adaptation planning and implementation in every sector and region, the growing resource gap necessitates more ‘effective’ climate adaptation projects. Against this backdrop, the purpose of this paper is to provide an exploratory analysis to identify and examine potential factors associated with the effectiveness ratings of multilateral-funded agricultural climate adaptation projects that have been implemented on the ground, completed and documented. Forty-four projects from multilateral funds were collected and analyzed for this purpose. The study employed a two-pronged approach to cross-complement the implications—key contributing factors to the effectiveness, rated per the actual outcome of the projects, were identified from the terminal evaluation documents of the highly satisfactory and unsatisfactory projects (conventional content analysis); and, effectiveness ratings were assessed against various socio-economic indicators of the countries where the projects were executed through Spearman’s correlation analysis to identify the possible association. The results implied that the contributing factors associated with the effectiveness ratings converge around several elements: (i) capacity building and education; (ii) local engagement and social inclusion; (iii) healthy and resilient livelihood; and, (iv) governance and commitment. Additionally, social inequality indicated its relevance to the project effectiveness ratings. While effectiveness ratings were found to have a positive and moderate correlation (r: 0.274; p < 0.1) with Inequality-adjusted Human Development Index (IHDI), such a correlation was not explicit with HDI. In addition, the study found strong correlations with multiple ‘inequality’ indicators—the gender inequality index, inequality-adjusted life expectancy index and inequality-adjusted income index. These results from the various ‘inequality-adjusted’ indexes further suggest the importance of considering all levels of the community, particularly those groups in the most disadvantageous positions, often farmers, to close the inequality gap. Overall, the findings and implications from this study are expected to provide a basis for future climate adaptation investments in agriculture.

Climate doi: 10.3390/cli14090179

Authors: Mehmet Ali Çelik Adile Bilik Yasin Paşa Ishak Pacal

Relative humidity (RH) provides complementary information on atmospheric moisture conditions that is not captured by precipitation alone. This study characterizes basin-scale RH-associated vegetation sensitivity across Türkiye’s 25 hydrographic basins using MODIS MOD13A1 NDVI observations for 2000–2024 and processed CMIP6-derived RH products for SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 at the 2040, 2060, 2080 and 2100 single-year horizons. MODIS and climate-model products are summarized independently at basin scale, avoiding pixel-level fusion across different native resolutions. The archived 25-by-4 pathway sensitivity matrix is evaluated using cross-pathway dispersion, a robust median-magnitude check, hierarchical clustering with silhouette diagnostics, and explicit percentile-based hotspot criteria. Mean absolute sensitivity is 0.0244, 0.0236, 0.0368, and 0.0386 under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. Antalya, Küçük Menderes, Konya Closed, Ceyhan, and Aras satisfy the magnitude and persistence criteria, and all five also exceed the upper-quartile threshold for median absolute sensitivity. Silhouette diagnostics do not support a unique three-cluster solution, favoring explicit threshold-based response classes. The results are interpreted as comparative basin screening rather than causal attribution or validated vegetation forecasting. Because the retained processed products do not preserve model/member provenance or the horizon-level intermediate values used to generate the pathway index, model-specific and predictive interpretations are deliberately avoided.

Climate doi: 10.3390/cli14090178

Authors: Xia Sheng Zheng Sheng Shengtao Feng

Stratospheric sulfate aerosol loading from volcanic eruptions conventionally perturbs planetary radiation budgets, inducing either global-scale cooling or regional thermal anomalies. The January 2022 eruption of Hunga Tonga–Hunga Ha’apai constituted a pronounced departure from this archetype. Here, ERA5 reanalysis products are leveraged to characterise thermodynamic and moisture perturbations at 0.01 hPa (~80 km) throughout the post-eruptive interval. The submarine event delivered approximately 146 Tg of water vapour into the stratosphere, with the plume ascending to 57 km and penetrating the tropical tropopause; a substantial fraction of this anomalous moisture subsequently persisted at mesopause altitudes, forming a reservoir that dissipates only gradually and perturbs upper-atmospheric chemistry across multi-year horizons. Nonlinear interactions between radiative forcing and dynamical feedbacks, both attributable to this exceptional water vapour burden, are elucidated. These observational constraints should advance understanding of upper-atmospheric responses to major volcanic events characterised by substantial water vapour emission.

Climate doi: 10.3390/cli14090177

Authors: Jonah Busch

The “climate bathtub,” originated by Linda Booth Sweeney and John Sterman in 2001, has provided an elegant metaphor for the physical problem of climate change for 25 years. In this metaphor, as long as inflows of water into a bathtub from a faucet (greenhouse gas emissions) exceed outflows through a drain (removals), then the stock of water in the bathtub (atmospheric concentrations) will rise, with eventual consequences when the bathtub overflows. This bathtub metaphor is simple, relatable, urgent, and apt. But climate change is a social problem, not just a physical problem. By adding four new elements to the climate bathtub—multiple faucets; multiple drains; multiple damage thresholds; and multiple people—the climate bathtub metaphor can illuminate a wide range of social challenges related to climate change. This expanded climate bathtub metaphor offers insights on how individuals prioritize climate actions; why collective disagreements emerge; and hierarchical levels of climate cooperation. It suggests heuristics that policymakers, practitioners, and future climate professionals can use to evaluate potential climate actions. This more complex, but more versatile, version of the climate bathtub can supplement the original metaphor as a public communications tool and motivator for climate action.

Climate doi: 10.3390/cli14090176

Authors: Dongliang Han Jianping Huang Lei Ding Guolong Zhang

In the Anthropocene, dryland ecosystems—natural and semi-natural ecosystems—are highly sensitive to anthropogenic warming, and their carbon cycling dynamics are easily disrupted by this trend. In this perspective, we propose a novel yet long-overlooked conceptual framework. It reveals that the oxygen footprint, defined as the atmospheric oxygen consumption-to-production ratio, could serve as an important diagnostic indicator for assessing the impacts of anthropogenic warming on dryland carbon cycling. This framework can be divided into three parts, including the increasing oxygen footprint, warming effects on dryland carbon cycling, and direct or indirect links between oxygen footprint and carbon-cycle responses. In short, it centers on the core logical chain: oxygen footprint-anthropogenic warming-dryland carbon cycling. This work strives to enhance dryland sustainability by filling essential knowledge gaps, combining separate research findings, and providing actionable field guidance for ecosystem management.

Climate doi: 10.3390/cli14090175

Authors: Carlos A. Pereira João Ferreira Vanda C. Pires Paula Drumond Eduardo Castanho Ricardo Deus Tânia Moura Rita M. Durão

The Portuguese agricultural sector has suffered a profound transformation over recent decades, evolving from traditional to increasingly technology-driven systems. Throughout this transition, climate and meteorological conditions have remained key drivers of agricultural productivity. Today, Portuguese agriculture faces growing challenges associated with climate change, including more frequent and intense heatwaves, droughts, and floods. Consequently, reliable climate information and decision-support tools are essential for strengthening resilience and promoting sustainable management. To address these needs, the Portuguese Institute for the Sea and Atmosphere (IPMA) developed two complementary climate service platforms for mainland Portugal: AgroClima and DataClima. The first provides observations from IPMA’s meteorological network, ECMWF forecasts, and agroclimatic indicators such as temperature, precipitation, soil water, and so-called agroclimatic warnings. The second offers historical climate information including WRFv4.2 simulations dynamically downscaled from ERA5 (1981–present), in situ observations (1941–present), and climate normals. Evaluation of the WRFv4.2 regionalization against IPMA observations shows a systematic underestimation of precipitation and air temperature, while mean wind speed is generally overestimated. Despite these biases, the downscaled WRFv4.2 dataset demonstrates sufficient accuracy to support operational climate services, providing valuable help for environmental monitoring, climate adaptation, and decision-making in agriculture and water resource management across Portugal.

Climate doi: 10.3390/cli14090174

Authors: Maite deCastro José González-Cao Nicolás G. deCastro María Cruz Gallego José M. Vaquero Ricardo M. Trigo Moncho Gómez-Gesteira

Early instrumental meteorological observations are essential for identifying and characterizing extreme climate events prior to the modern observational era, particularly in regions where historical data are scarce. This study documents a remarkable late-spring heatwave using newly recovered daily temperature observations from Ferrol (northwestern Spain) for the period 1792–1795, retrieved from the Historical Archive of the Royal Institute and Observatory of the Spanish Navy. Despite the temporal limitations of this early dataset, its daily resolution enables a detailed analysis of short-term climatic variability and extreme temperature events. The analysis reveals an extraordinary warm episode in late May 1795, characterized by sustained positive temperature anomalies that stand out clearly against the surrounding days. Comparison with the modern climate indicates that the event was exceptional even by present-day standards, with mean temperatures approximately 2 °C above the current 95th percentile. To assess the spatial extent of the heatwave, we compared the Ferrol data with contemporaneous instrumental records from Madrid, Barcelona, and Cádiz. All three locations exhibit synchronous, pronounced positive anomalies, demonstrating that the 1795 event was a large-scale phenomenon affecting most of the Iberian Peninsula. This study highlights the critical value of historical data rescue for contextualizing modern climate extremes and extending our understanding of regional climate variability.

Climate doi: 10.3390/cli14090173

Authors: Patricia Ann A. Jaranilla-Sanchez Hanz Lester C. Lunas Catherine B. Gigantone Michael Jason L. Mozo Emmanuel Zeus S. Gapan Keane Carlo G. Lomibao Allan T. Tejada Rodel D. Lasco

The rising global temperature and changing climate patterns have increased the frequency and intensity of extreme heat events, droughts, and heavy precipitation, significantly affecting agriculture, water resources, and ecosystems. Solar radiation management (SRM) has been proposed as a geoengineering strategy to mitigate these effects by reducing incoming solar radiation. This study evaluated future trends and variability in rainfall and temperature extremes in the Philippines under GeoMIP (G6Solar and G6Sulfur) and ScenarioMIP (SSP2-4.5 and SSP5-8.5) projections. Using five General Circulation Models (GCMs) and a suite of 10 climate indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), changes in extreme precipitation and temperature across different climate zones in the Philippines were assessed. Climate projections for the future (2041–2070) scenario were analyzed using bias correction, downscaling, and spatial interpolation techniques. Trend analysis was evaluated using the Mann–Kendall test and Sen’s slope estimator, while variability was assessed through statistical methods. The results show widespread warming and increased extreme precipitation, but these trends vary significantly across regions. Non-uniform responses emerge across scenarios, with some northern regions experiencing decreases in specific precipitation indices despite the broader warming trend under SRM and non-SRM conditions. These findings provide critical insights into the potential impacts of SRM on future climate extremes in the Philippines and guidance on climate policy recommendations for decision-makers and stakeholders.

Climate doi: 10.3390/cli14090172

Authors: Faustin Katchele Ogou Khadija Arjdal Fatima Driouech

Climate change and variability pose serious threats to natural and human systems. The Mediterranean and North Africa (MNA) are among the world’s climate change hotspots. An in-depth understanding of the decadal climate variability in this region is critical to support planning and management, as well as adaptation in important sectors such as water resources. Therefore, in this study, fifth-generation ECMWF atmospheric reanalysis (ERA5) precipitation data and the outputs of the Coordinated Regional Downscaling Experiment-COmmon Regional Experiment (CORDEX-CORE) regional models were used to characterize the decadal precipitation variability in MNA and its sub-regions (Western North Africa: WNA, Sahara: SAH, southern Mediterranean: SMED, and northern Mediterranean: NMED). The models showed overestimation in most areas and underestimation in a few areas relative to the ERA5 data, with the magnitude varying by region and season. The positive biases obtained from the regional climate models (RCMs) were higher than the positive biases obtained from the general circulation models (GCMs). The wet biases were dominant during the annual, summer, and autumn seasons over MNA and its sub-regions. Negative biases were mostly associated with GCMs, mainly HadGEM2-ES and/or NorESM1-M; meanwhile, they were linked with RCMs such as CCLM5-0-15 and/or RegCM4_v7 and were mostly obtained in winter and spring. The multi-model mean (MME) was better at reproducing the decadal precipitation patterns over MNA, SMED, and NMED at all time scales, while REMO2015-NorESM1-M and the MME performed better than the remaining models at the annual time scale over WNA and SAH. These findings are useful for improving climate modeling, the water resources management and related sectors, and climate adaptation strategies in the region, especially in North Africa. The short-period coverage of the simulated data available for this study constitutes a limitation to the findings.

Climate doi: 10.3390/cli14090171

Authors: Aziz Razzouki Mounsif Ridaoui Fadma Razzouki Mohamed Oudgou Mustapha Ouatmane Abdeslam Boudhar

Climate change is becoming an increasingly important source of economic and health vulnerability in developing countries. This study examines the dynamic relationship between climate change and poverty across 22 countries in the Middle East and North Africa (MENA) region from 2000 to 2023. Poverty is captured through two indicators: household consumption expenditure, the monetary dimension, and infant mortality, the non-monetary dimension. Methodologically, the analysis relies on a panel autoregressive distributed lag (panel ARDL) model, estimated using the Pooled Mean Group (PMG) and Mean Group (MG) approaches. The results reveal a long-run relationship among climatic variables, macroeconomic factors, and poverty-related indicators. In the long run, precipitation is associated with a decline in household consumption expenditure, while temperature is associated with higher infant mortality, indicating a deterioration in both monetary and health-related well-being under changing climatic conditions. In the short run, rising temperatures are also associated with lower household consumption expenditure, revealing the immediate vulnerability of living standards to climate shocks. In addition, GDP per capita is associated with higher household consumption and lower infant mortality, while education is associated with lower health-related poverty. Inflation appears to exacerbate poverty, whereas the positive association between health expenditure and infant mortality suggests reverse causality or inefficiencies in the allocation of health resources. These findings highlight the need to articulate climate adaptation strategy, macroeconomic stability, education investment, and improved efficiency of health spending in order to achieve sustainable reduction in poverty in the MENA region.

Climate doi: 10.3390/cli14080170

Authors: Pussadee Laor Wanvisa Saisanan Na Ayudhaya Sirinthip Rakjun Pannipha Dokmaingam Vivat Keawdounglek Anuttara Hongtong Weerayuth Siriratruengsuk Krailak Fakkaew Thitipong Sukdee Rohmatul Fajriyah

Rice farmers in Chiang Rai are increasingly exposed to recurrent drought under climate change. Although previous studies have applied Knowledge–Attitude–Practice (KAP) frameworks to drought adaptation, evidence on ageing farming populations and the interaction between behavioral and institutional factors remains limited. This study aimed to identify behavioral and institutional factors associated with drought adaptation practices among rice-farming households. A cross-sectional study was conducted among 180 rice-farming households. Quantitative data were collected using a structured questionnaire validated through expert review (IOC) and pilot testing to assess socio-demographic characteristics and drought-related perception, knowledge, attitudes, and practices (KAP). Pearson correlation and binary logistic regression were applied to examine relationships and factors associated with higher drought adaptation practices. Qualitative data from open-ended questionnaire responses regarding government support and drought management recommendations were analyzed thematically to provide contextual insights into farmers’ experiences and adaptation challenges. Farmers demonstrated high drought perception (M = 4.86 ± 1.05/5) but only moderate knowledge (16.75 ± 3.36/25), attitudes (2.77 ± 1.33/5), and practices (22.57 ± 6.04/35). Knowledge was positively associated with adaptation practices (r = 0.224, p < 0.01), whereas perception showed no significant direct association, indicating a gap between awareness and adaptive action. Regression analysis showed that higher drought-related knowledge (aOR = 4.60, p = 0.003) and prior water scarcity experience (aOR = 4.45, p = 0.002) were significantly associated with higher drought adaptation practices. In contrast, older age, male gender, and off-farm employment were significant constraints. Qualitative findings highlighted persistent concerns regarding the adequacy, timeliness, and equity of government support, suggesting that institutional barriers limited farmers’ ability to translate awareness into adaptive action. These findings indicate that both behavioral and structural factors shape drought adaptation practices. While drought-related knowledge represents an important component of adaptive capacity, effective adaptation also depends on institutional support and resource accessibility. Strengthening agricultural extension services and improving the responsiveness and equity of government support may enhance the adaptive capacity and resilience of smallholder rice farmers in drought-prone regions.

Climate doi: 10.3390/cli14080169

Authors: João Carlos de Sousa Luísa Schmidt

Expectations regarding collective climate action are multidimensional and should be understood in relation to the social contexts in which they are embedded. This study examines whether these expectations are associated with the country-level average belief in the anthropogenic origin of climate change and with individuals’ deviation from that average. Drawing on Round 10 of the European Social Survey, the analysis utilizes 24,014 valid cases from 29 European countries. It estimates Complex Samples General Linear Models (CSGLM) to account for the complex sampling design. The results show that the country-level average belief in the anthropogenic origin of climate change is positively associated with expectations of collective climate action; that is, countries with a higher average belief exhibit higher expectations of collective climate action. Conversely, individuals whose belief in the anthropogenic origin of climate change exceeds the respective country-level average tend to express lower expectations of collective climate action. This negative association becomes stronger at higher levels of climate worry and is attenuated as ideological positioning moves towards the right; in contrast, trust in scientists does not have a significant moderating effect. Overall, the findings support a socially contextualized understanding of expectations of collective climate action, in which both national belief contexts and individuals’ relative positioning within those contexts are relevant.

Climate doi: 10.3390/cli14080168

Authors: Waléria Pereira Monteiro Corrêa Leonardo de Sousa Miranda Luciano Jorge Serejo dos Anjos Thaiane Soeiro da Silva Dias José Felipe Gazel Menezes Everaldo Barreiros de Souza

Climate change is expected to significantly alter surface air temperature and precipitation regimes across the Amazon Basin, with direct implications for agroforestry systems and climate-sensitive perennial crops. This study projects the impacts of anthropogenic global warming on the habitat suitability of Theobroma grandiflorum (cupuaçu), a keystone species for Amazonian agroforestry, local livelihoods, and the bioeconomy. Using an ensemble species distribution modeling (SDM) framework, we applied multiple algorithms and two CMIP6 global climate models (BCC-CSM2-MR and MIROC6) under the SSP3-7.0 and SSP5-8.5 scenarios for the near-future (2021–2040) and far-future (2061–2080). Contrary to the range contractions projected for many Amazonian endemics, our ensemble projections reveal a consistent trend of expanding climatically suitable area, primarily into adjacent ecological transition zones. This expansion is driven largely by basin-wide increases in mean annual temperature, the most influential predictor in our modeling study. Quantitatively, we project a net habitat expansion of 32.2% (415,114 km2) under high-concordance future scenarios. A core climate refuge of 955,570 km2, representing 74.2% of the current suitable range, is identified as a high priority for in situ conservation. Conversely, approximately 25.8% of the current range (331,919 km2) may become climatically unsuitable, particularly in parts of the western Amazon. These findings suggest that reducing thermal constraints in currently marginal areas could open opportunities for integrating cupuaçu into climate-resilient agroforestry systems beyond its present distribution. Such heterogeneous responses of Amazonian agroforestry species to climate change highlight the importance of incorporating species-specific climate sensitivities into adaptation planning, land-use strategies, and climate-resilient bioeconomy policies under future warming scenarios.

Climate doi: 10.3390/cli14080167

Authors: Emmanouela Kamperi Apostolos-Emmanouil Bazanis Konstantinos Bertsouklis

Climate change increasingly threatens Mediterranean ornamental horticulture and green infrastructure through elevated temperatures, prolonged drought conditions, soil salinity, and more frequent extreme weather events. As a result, plant growth, phenology and landscape sustainability are significantly affected. This systematic review aimed to identify and qualitatively synthesize the available evidence on the responses of ornamental plants and their production and end use systems to climate-related stress, with emphasis on Mediterranean native species and their potential contribution to climate-resilient landscaping. The review was conducted and reported in accordance with PRISMA 2020. An adapted Population–Exposure–Outcome framework was used to operationalize the overarching review question and guide eligibility assessment. Scopus and the Web of Science Core Collection were systematically searched for peer-reviewed English-language articles published between 1 January 2001 and 31 May 2026. Eligible publications examined ornamental plants, floricultural species, or native and endemic taxa with potential ornamental or landscape use and addressed climate-related stressors, plant resilience, adaptation strategies, cultivation or propagation practices, green-infrastructure applications, or related ecological trade-offs in Mediterranean-relevant contexts. Two reviewers independently assessed titles, abstracts, and full texts using predefined eligibility criteria. The review used a structured qualitative narrative synthesis organized into thematic domains to systematically identify, select, and synthesize the available evidence. Meta-analysis was not undertaken because of substantial heterogeneity in plant material, environmental stressors, study designs, and reported outcomes. A total of ninety studies were included and organized into five domains: climate stress and plant responses (n = 14), native Mediterranean ornamental species (n = 21), adaptation and resilience strategies (n = 16), urban landscaping and green infrastructure (n = 25), and ecological risks and invasive species (n = 14). The review revealed that several native Mediterranean plants possess morphological, physiological, or ecological characteristics associated with tolerance to drought, salinity, and other climate-related stresses, supporting their potential use in sustainable ornamental horticulture. Water-efficient irrigation, alternative water sources and substrates, nursery preconditioning, non-microbial biostimulants, and genotype or physiological screening showed adaptation potential, but their effectiveness depended on species, genotype, intervention intensity, and application context. Evidence remained limited for compound stresses, combined interventions, nursery-to-landscape transfer, long-term field performance, commercial scalability, and environmental trade-offs. Overall, climate-resilient ornamental horticulture requires the integration of plant selection, propagation, production, controlled stress screening, landscape validation, and ecological-risk assessment. This review proposes an evidence-to-application framework to support research, nursery production, landscape planning, and the responsible deployment of climate-adapted ornamental plants.

Climate doi: 10.3390/cli14080166

Authors: Fumiaki Fujibe

Changes in the thickness temperature (TT) of the lower atmosphere from 1961 to 2025 were investigated using air pressure data from eight stations in the Central Mountain region of Japan. The stations were located at elevations ranging from 400 m to 1300 m above sea level. The average linear trend in TT at these stations was 0.25 °C per decade, which was close to the air temperature trend at non-urban stations in the surrounding coastal areas. Despite the presence of some divergence in TT trends across the stations, TT is expected to serve as a complementary index for climate change research, since concerns about spatial representativeness and temporal homogeneity exist in air temperature data.

Climate doi: 10.3390/cli14080165

Authors: Gustavo De la Cruz Eduardo Chávarri-Velarde Waldo Lavado-Casimiro

Extreme climate events, particularly precipitation extremes, pose significant risks to ecosystems, infrastructure, and socio-economic systems globally. In Peru, the diversity of its climate, driven by its complex topography, makes it highly vulnerable to such events, especially in the Andes and Amazon regions. This study evaluates the performance of 25 CMIP6 GCMs in simulating extreme precipitation events during both the wet and dry seasons at the national level. Gridded precipitation data from the PISCO product and CMIP6 model simulations for the period 1981–2014 were used to estimate extreme precipitation indices, including Rx1day, Rx5day, SDII, CDD, CWD, R10mm, and PRCPTOT. Performance was assessed using statistical metrics such as PBIAS, NRMSE, and the Pattern Correlation Coefficient (PCC), integrated through a TOPSIS ranking. Results indicate that NorESM2-MM, MPI-ESM1-2-LR, and CESM2 exhibit the best performance, achieving TOPSIS scores above 0.8. These models show high spatial correlation (PCC frequently >0.8) and relatively low biases. In contrast, models like FGOALS-g3 and CanESM5 show significant limitations, with PBIAS exceeding 80% in Rx1day and Rx5day and TOPSIS scores below 0.5. The ensemble reveals a persistent ‘drizzle bias,’ with wet day frequency (R1mm) generally overestimated by 20–40% in the wet season and by 40–80% during the dry season across most CMIP6 models. Furthermore, indices of temporal persistence (CWD and CDD) remain the most challenging, with CWD overestimations often exceeding 100–200%. These findings highlight the critical need for statistical or dynamical downscaling, together with bias correction, before using CMIP6 projections for local adaptation strategies in the Andes and Amazon regions.

Climate doi: 10.3390/cli14080164

Authors: Koffi Evrard Brou Yao Etienne Kouakou Brama Koné Iba Dieudonné Dely Madina Doumbia Aristide Gountôh Douagui Stanley Luchters Matthew Francis Chersich Guéladio Cissé

Extreme heat events are associated with increased morbidity and mortality, disproportionately affecting vulnerable populations. However, evidence from disadvantaged urban settings in Africa remains scarce. This systematic review aimed to synthesize existing research on the health impacts of extreme heat in Africa’s disadvantaged urban communities. A systematic literature search was conducted in accordance with PRISMA guidelines across eleven databases (PubMed, Cochrane, VHL, Google Scholar, ScienceDirect, Research4Life, DOAJ, Emerald, Taylor & Francis, Annual Reviews, and IRIS), carried out over two search periods (December 2023–March 2024 and December 2024–February 2025). Eligible studies were published between 2010 and 2024 in English, French, or Portuguese, and examined direct or indirect heat-health effects in disadvantaged urban settings. Screening of 96,608 titles and 59 abstracts yielded 21 eligible studies. Geographically, the evidence was concentrated in South Africa (n = 7), Kenya (n = 6), and Tanzania (n = 4), with single studies from Cameroon, Egypt, Nigeria, and Ghana. Heat-related morbidity was examined in 17 studies and mortality in four. Commonly reported conditions included dehydration, heat exhaustion, skin rashes, heatstroke, and cardiovascular and respiratory diseases. Extreme heat was further associated with mortality from non-communicable diseases, pneumonia, and acute respiratory infections. Children, older adults, and the most economically deprived communities exhibited heightened vulnerability, driven by adverse socioeconomic and environmental conditions. The available evidence confirms substantial heat-health impacts on disadvantaged urban populations across Africa, while revealing a notable research gap in French- and Portuguese-language literature. These findings underscore the urgent need for expanded, geographically and linguistically diverse research to inform climate adaptation strategies and health protection policies.

Climate doi: 10.3390/cli14080163

Authors: Evangelos Leivadiotis Aris Psilovikos Mohamed Elhag

Anthropogenic climate change has disrupted the global hydrological cycle, increasing compound extreme events like Dry–Wet Abrupt Alternations (DWAAs). Regarding the Mediterranean Basin, Greece is highly susceptible to these abrupt hydroclimatic shifts, which frequently overwhelm reactive disaster management. This study quantifies the spatiotemporal dynamics of DWAA events across Greece from 1990 to 2024. Using the 1-month Standardized Precipitation Evapotranspiration Index (SPEI-1) from ERA5 reanalysis, transitions were classified into dry-to-wet (DW) and wet-to-dry (WD) across moderate (±1.0), severe (±1.5), and extreme (±2.0) thresholds. Core physical metrics (duration, severity, and intensity) were evaluated using Anselin Local Moran’s I (LISA) and Mann–Kendall tests to identify spatial hotspots and temporal trends. Results revealed a spatially decoupled hazard regime dictated by topography and atmospheric mechanics. Severe DW transitions primarily manifest as intense autumn flash floods (62.7%) concentrated in western and southern districts. Conversely, severe WD transitions emerge as high-magnitude summer agricultural flash droughts (52.5%) clustered in central and northern continental plains. Crucially, while the magnitudes of these events demonstrate historical temporal stationarity, their decadal frequency doubled in the 2020s. This increase validates the idea that global warming accelerates systemic climate extremes, necessitating an urgent shift toward proactive, highly localized adaptation strategies.

Climate doi: 10.3390/cli14080162

Authors: Dario B. Giaiotti Francesca Zarabara

Regional climate models (RCMs) remain affected by limited spatial resolution and systematic biases relative to observations, posing a major challenge for the provision of actionable climate information at the local scale. Bridging the gap between coarse, biased model outputs and site-specific climate needs is therefore both urgent and an active area of research. Here, we present a diagnosis of near-surface air temperature biases, based on the vertical structure of the atmospheric column, in an ensemble of EURO-CORDEX simulations over a complex Alpine region. We show how the conventional and commonly applied temperature bias correction, aimed at removing the discrepancy between the model representation of orography and the actual terrain elevation, still leaves biases of a magnitude comparable to or even greater than the applied correction. After the orographic correction, residual biases originate from both free-atmosphere temperature biases and low-level biases. Each contribution to the TAS bias is quantified through an inter-model seasonal analysis. This study highlights the limitations of RCMs in representing complex orography and boundary-layer conditions in Alpine regions. It also provides interpretative keys for a better understanding of the underlying site-specific physical causes of TAS biases and cautions against the potential pitfalls of a straightforward application of bias-correction methods.

Climate doi: 10.3390/cli14080161

Authors: Georgy I. Shapiro Mohammed Salim

This study examines interannual variability of coastal upwelling in the Lakshadweep Sea (2015–2020) using a high-resolution (1/20°) NEMO model within a novel two-step data assimilation framework. Pre-monsoon conditions show limited interannual differences, whereas the mature monsoon phase reveals substantial variability in the spatial extent and persistence of cold, dense upwelled waters within the euphotic zone. The strongest upwelling occurred in 2016 and 2018, with weaker conditions in 2015, 2017, 2019, and 2020. The new Strength of Upwelling (SoU) index, defined as the temporal integral of the area occupied by waters colder than a specific threshold (26 °C in this case) within a coastal band, quantifies upwelling intensity and duration directly from modelled hydrographic fields. Subsurface metrics at 14 m depth provide clearer interannual discrimination than surface indicators alone. Latitudinal analysis reveals spatial heterogeneity in upwelling intensity, suggesting that local processes modulate large-scale monsoon forcing. Ultimately, the SoU index offers a new physically based tool for assessing upwelling variability relevant to ecosystem dynamics and fisheries management.

Climate doi: 10.3390/cli14080160

Authors: Danielle Elis Garcia Furuya Édson Luis Bolfe Victória Beatriz Soares Franco da Silveira Jayme Garcia Arnal Barbedo Luciano Gebler

Hail events represent a major source of economic risk in apple production, leading to significant losses in yield and marketable output. Although anti-hail net systems are increasingly adopted as a mitigation strategy, evidence on their economic viability remains limited, especially in emerging regions. This study examines the economic dimensions of anti-hail net adoption in apple orchards in Vacaria, southern Brazil, by integrating remote sensing data, official loss records, and producer interviews. Multitemporal Sentinel-2 imagery was used to map the expansion of net-covered areas between 2016 and 2026. Economic loss data from eight recorded hail events, obtained from official government sources, were analyzed, along with apple area and production data. Exploratory interviews provided insights into installation costs, production losses, and perceived benefits. Results indicate a clear expansion of anti-hail nets associated with substantial hail losses. Producers reported pre-adoption losses of 30–90%, installation costs of Brazilian Real (BRL) 40,000–55,000 per hectare, and estimated payback periods of 3–4 years. These findings highlight the value of integrating remote sensing, official disaster records, and producer knowledge to support exploratory climate adaptation and economic assessments.

Climate doi: 10.3390/cli14080159

Authors: Anja Pejović Riccardo Pollo

To mitigate the health and environmental risks of urbanisation and the urban heat island effect, urban planning is shifting towards promoting active mobility. The success of these efforts largely depends on understanding people’s thermal comfort on the move, driving research towards the investigation of the complex interdependencies between microclimatic conditions and the real-time experiences of pedestrians and cyclists. This literature review aims to present the state of the art of research at the neighbourhood and street levels by analysing the methodological frameworks employed and the outcomes achieved. The paper adopts a thematic clustering approach, grouping the articles on the basis of their research objectives, methodologies and the relationships between active mobility and comfort. The literature review highlights a shift from static to dynamic comfort assessments and a focus on active travel as a continuous experience rather than the sum of stationary moments. The primary findings include the consolidation of the thermal walk methodology and the emergence of cumulative stress indices. Evidence from heat stress contexts suggests that pedestrians prefer thermal diversity, which causes thermal alliesthesia, over monotonous conditions. The research highlights the role of heat stress as a barrier to walkability in hot and temperate climates, causing route deviations and lower walking speeds, with the opposite pattern in severely cold settings. This review identifies several research gaps, including a lack of standardised dynamic assessment methods, low generalisability and transferability of the results, and insufficient investigation of cyclists’ dynamic comfort compared with that of pedestrians.

Climate doi: 10.3390/cli14080158

Authors: Nektarios N. Kourgialas

Hydroclimatic evidence is often carried too directly from global-scale attribution studies into local water-resources decisions, despite important differences among variables, methods, and spatial scales. This critical narrative review examines how climate variability, statistical trends, detection, attribution, non-stationarity, and risk should be distinguished when interpreting changes in precipitation, drought, streamflow, floods, groundwater, and water availability. The review compares global assessments, Mediterranean studies, and selected local examples to clarify what each line of evidence can—and cannot—support in adaptation planning. Human influence on global warming is unequivocal, and increases in atmospheric evaporative demand are well supported across many regions; anthropogenic influence has also been detected in several large-scale water-cycle responses. Historical changes in precipitation, river flooding, groundwater, and local drought remain spatially heterogeneous because internal variability interacts with circulation, storage, landscape properties, abstraction, infrastructure, and demand. Statistically significant trends do not by themselves establish hydrological importance or causation, while non-significant local trends do not imply an absence of operational risk. On this basis, the review proposes a scale-aware way of matching hydroclimatic evidence with system vulnerability and the degree of commitment involved in adaptation. Low-regret and adjustable measures can address current vulnerabilities under uncertainty, whereas costly, long-lived, or difficult-to-reverse interventions require stronger local evidence and stress testing across plausible futures.

Climate doi: 10.3390/cli14080157

Authors: Haider Taha

Many airports now consider implementing urban-cooling measures as proven strategies to reduce energy use and improve environmental conditions. Studies have also shown that reflective surfaces on roofs and grounds can have beneficial effects on thermal environment and airport workers’ productivity. To date, no studies have been undertaken to specifically assess whether any effects on aircraft performance would also result from implementing these measures. In this exploratory study, high-resolution micrometeorological modeling and remote-sensing analysis were undertaken to quantify the potential reductions in fuel use and, thus, emissions from aircraft taxiing on cooler surfaces. The results suggest small but non-zero benefits in terms of emissions and takeoff-roll distances. Using the Dallas–Ft. Worth International Airport (DFW) as a case study and Boeing 737 aircraft type as an example, it is found that if taxiways and shoulders albedo is increased to 0.35, an average of 2.3 kg CO2 can be saved per single taxi-out or taxi-in operation around midday and about 1.5 kg CO2 earlier in the morning or later in the evening. It is also found that even though runways albedo remains unchanged, cooler air advected over runways from modified taxiways can reduce the takeoff-roll distance by an average of 3% around midday that tapers off to an average of 1% early in the morning or late evening. Indeed, these are small effects per single taxi-in or taxi-out operation but when scaled by some 2000 arrivals and departures per day at DFW, saving 4000–5000 kg CO2 per day, and if further scaled by the number of eligible airports, the impacts become significant. Furthermore, the effects reported here are from taxiway and shoulder modifications alone; if combined with the effects from cool roofs and other cool ground surfaces at terminals, tarmacs, ramps, and parking areas, the benefits will add up significantly. Limitations in this study, that would be addressed in future work, include simplifying assumptions regarding aircraft-engine performance, specifications, and aircraft types mix and operations.

Climate doi: 10.3390/cli14080156

Authors: Carolina Rodríguez Jennyfer Serrano Eduardo Leiva

Prolonged drought in Chile has imposed increasing pressures on water-dependent tourism activities, although its effects have been assessed only fragmentarily and rarely linked to tourism-relevant indicators. This study provides an integrated hydro-climatic and economic assessment of drought impacts on two tourism categories especially sensitive to water availability: snow and mountain tourism, and tourism related to water bodies and watercourses. For this purpose, time series of snow cover, streamflow, precipitation, and water quality were analyzed for 2000–2024, complemented by sectoral statistics and indirect indicators of economic impact. Trend analyses used linear regression, the Mann–Kendall test, Sen’s slope, Pettitt change-point detection, and Spearman correlations between hydroclimatic variables and tourism proxies. Results show a significant decline in snow cover across most of northern and central Chile, with strong signals in basins critical for winter tourism and a common temporal break in 2009. Widespread streamflow reductions were also detected in rivers from central, southern, and Patagonian Chile, although with differing magnitude and timing. In contrast, water-quality changes were limited and spatially heterogeneous. In the ski sector, reduced snow accumulation was associated with shorter ski seasons, fewer skier-days, and lower direct employment. For rafting, declining streamflow was associated with reduced hydrological suitability, indicating less favorable potential operating conditions. Overall, drought affects tourism significantly but unevenly, depending on geography, hydrological regime, activity type, and data availability. The proposed integrated assessment helps identify differentiated drought-impact pathways and supports more climate-resilient tourism management.

Climate doi: 10.3390/cli14080155

Authors: Nurul Amalina Khairul Hasni Nadia Mohamad Raheel Nazakat Imanul Hassan Abdul Shukor Sharifah Mazrah Sayed Mohamed Zain Siti Aishah Rashid Noraishah Mohammad Sham Nik Muhammad Nizam Nik Hassan Mohamad Iqbal Mazeli Mohd Redzuan Zainudin Thahirahtul Asma’ Zakaria Rohaida Ismail

Climate-related hazards are occurring with increasing frequency, resulting in notable disruptions to healthcare systems. In Malaysia, healthcare facilities are particularly impacted by flooding and heatwaves, which can occur annually across some regions. Despite these recurrent challenges, there remains limited availability of a standardized tool to systematically assess healthcare facility (HCF) vulnerability to climate hazards. This study aimed to develop, validate and conduct a field testing of the Vulnerability Index Tool for Assessing Levels of Climate Resilience in Healthcare Facilities (VITAL-HCF) for facility-level assessment in Malaysia. The VITAL-HCF was developed through extensive literature review, experts consultations, and adaptation of the World Health Organization (WHO) healthcare facility vulnerability checklist. The tool underwent forward and backward translation to ensure linguistic and contextual equivalence, followed by content and face validation by subject-matter experts in climate and healthcare professionals. Subsequently, field testing was performed in three government healthcare facilities to assess the clarity, applicability, and feasibility of administration to ensure accurate responses representing facility-level capacity and vulnerability. The healthcare facility vulnerability index (HCFVI) for heatwaves and flooding was then calculated for each facility. Revised Scale-Level Content Validity Indices (S-CVI/Ave) varied across the exposure, sensitivity and adaptive capacity domains (0.93–1.00). Items with a content validity index < 0.83 were either removed or revised and reorganized to improve relevance. Face validation showed good clarity with S-FVI/Ave ≥ 0.87. The final tool comprised 12 exposure, 11 sensitivity, and 181 adaptive capacity indicators. Field testing showed that a facilitated, multidisciplinary group approach among key respondents was feasible and timely. Facility A, located in an urban setting, had high vulnerability for hot weather and heatwaves (HCFVI = 0.51), while facilities B and C recorded moderate vulnerability. Both Facilities A and B recorded moderate vulnerability for floods, while Facility C, a hospital in an urban setting, had low vulnerability (HCFVI = 0.23). The VITAL-HCF demonstrated satisfactory content validity, face validity, and feasibility for assessing climate-related vulnerability in HCFs. The tool incorporates key vulnerability components of exposure, sensitivity, and adaptive capacity, providing a structured approach for the systematic assessment of climate-related vulnerability in healthcare facilities to support targeted preparedness and resilience planning.

Climate doi: 10.3390/cli14080154

Authors: Ioannis Masloumidis Antonios Bezes Konstantinos Lagouvardos Ioannis Koletsis Vassiliki Kotroni Christos J. Lolis Silvio Davolio Andrea Buzzi

Wind influences human activities both directly and indirectly. Directly, it affects, among others, transportation and wind energy systems through its direction and intensity, while extreme wind events can cause severe damage to infrastructure and buildings and even casualties. Indirectly, the movement of air masses is strictly associated with all meteorological phenomena, highlighting the crucial role of wind in shaping weather conditions. In the context of climate change, anomalies in global and regional circulation patterns modify the characteristics of surface winds. Consequently, investigating long-term wind variability and trends is essential for assessing climate change impacts on the environment and society. The climatology of near-surface (10 m) winds over Greece for the period 1991–2020 is examined using a high-resolution regional reanalysis dataset, focusing on the mean wind speed, mean daily maximum wind gust, and the frequency of strong-wind days. The results reveal substantial spatial and temporal variability, with the most pronounced upward trends of these parameters observed over the Aegean Sea and northeastern Greece. Statistically significant trends are detected mainly during winter and summer. In particular, January and August exhibit the strongest positive trends, locally exceeding 0.05 m s−1 per year for mean wind speed and 0.1 m s−1 per year for mean daily maximum wind gust. Moreover, the frequency of strong-wind days increases in several regions with local trends exceeding 0.2 days per year. These findings highlight the value of high-resolution regional reanalyses for characterizing near-surface wind variability and trends over areas of complex terrain.

Climate doi: 10.3390/cli14080153

Authors: Susan. G. Lakkis Mariana Barrucand Adrián. E. Yuchechen Pablo. O. Canziani

A 117-year daily temperature record (1903–2019) from La Rioja, Argentina, is constructed and homogenised by combining rescued historical observations from the archives of the Oficina Meteorológica Argentina (1903–1940) with modern operational data from the Servicio Meteorológico Nacional (1941–2019). Analysing such extensive historical records is essential for identifying long-term temperature trends and for understanding recent changes within the context of the climate system’s natural variability. This record is then examined to characterise centennial trends, multidecadal variability, extreme temperature events, and the association with major modes of Pacific and Atlantic oceanic variability. Over the full period, no significant trends were found in the maximum (Tmax) or minimum (Tmin) temperature series or in the diurnal temperature range (DTR). Since the mean series show no significant trend, the dominant pattern of variability was characterised through Lomb–Scargle spectra of the daily anomaly series, which revealed dominant multidecadal peaks of ~37 years for Tmax and DTR and around 27 years for Tmin. Cross-correlation with the oceanic indices shows that Tmin is weakly but consistently associated with two of the considered tropical Pacific indices, while Tmax appears to be decoupled from the four considered modes. The frequency of warm days declines significantly, at −3.82% per century, despite the stationary Tmax mean, indicating a contraction of the upper tail of the daytime distribution. A pronounced day–night asymmetry runs through all diagnostics. The findings illustrate the importance of complementing linear trend analysis with spectral diagnostics in records exhibiting strong multidecadal variability and contribute a long-term reference for a previously underexplored semiarid station.

Climate doi: 10.3390/cli14070152

Authors: Konstantina Koutroumanou-Kontosi Panos Hadjinicolaou Constantinos Cartalis Jos Lelieveld

This study investigates the potential of machine learning-based statistical downscaling to generate high-resolution (1 km) urban climate projections over Nicosia, Cyprus. A dynamical and a hybrid dynamical–statistical framework was applied to CMIP6 data under the SSP2-4.5 scenario to assess mid-twenty-first-century summer thermal conditions, focusing on daily maximum (T2max) and minimum (T2min) 2 m air temperature. The WRF model was used to dynamically downscale ERA5 reanalysis data to produce reference datasets for training three statistical models of increasing complexity: Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN). All models successfully reproduced the spatial temperature patterns simulated by WRF, with the CNN achieving the best performance and improved spatial consistency. Evaluation against observations confirmed that the statistical models capture daily maximum and minimum temperature variability with accuracy comparable to the dynamical model. Both the WRF model and the trained statistical models were subsequently applied to CMIP6-driven predictors to produce high-resolution projections. Results indicate pronounced summer warming over Nicosia by mid-century (1.9 °C for T2max and 1.1 °C for T2min), with strong spatial heterogeneity and intensified heat hotspots in compact urban areas (LCZ 3). These findings highlight the value of integrating dynamical and machine learning approaches for efficient urban-scale climate assessments.

Climate doi: 10.3390/cli14070151

Authors: Alexandre Lefevre Bruno Malet-Damour Harry Boyer Garry Rivière

Dense tropical cities face increasing outdoor heat stress and require reliable microclimate modelling tools to support climate-responsive urban planning. However, the performance of these models remains insufficiently documented under tropical conditions. The objective of this study is to experimentally validate the performance of ENVI-met for outdoor thermal comfort assessment in a dense tropical neighbourhood on Reunion Island. Validation was performed using a network of 11 low-cost weather stations distributed across the study area, providing observations of air temperature, relative humidity, wind speed and globe temperature, from which mean radiant temperature (MRT) and the Universal Thermal Climate Index (UTCI) were derived. Model performance was evaluated through multipoint validation, error-propagation analysis and sensitivity experiments. Results show that ENVI-met reproduces air temperature and relative humidity with good accuracy (RMSE of 1.2 °C and 5%), while larger uncertainties are observed for wind speed (up to 4 m s−1) and MRT (up to 8 °C), with strong spatial variability. Error-propagation analysis indicates that 62% of the UTCI error variance is explained by combined microclimate simulation errors, with radiative conditions emerging as the dominant contributor. Sensitivity analyses identify boundary wind forcing as the main source of modelling uncertainty. Overall, the proposed validation framework provides practical guidance for improving ENVI-met applications in tropical urban environments and supports more robust climate-responsive urban design.

Climate doi: 10.3390/cli14070150

Authors: Rong Hu Ramesh P. Rudra Ashok Shaw Rituraj Shukla Pradeep Goel

This study investigates historical and future climate variability in the Canagagigue Creek Watershed, located in southern Ontario, through statistical downscaling and trend assessment of key climate variables. Future climate projections from the Canadian Global Circulation Model (CGCM2) under SRES A2 and B2 emission scenarios were downscaled using the Statistical Downscaling Model (SDSM) to generate daily maximum temperature (Tmax), minimum temperature (Tmin), and precipitation series for the future period of 2025 to 2044. Historical observations were used to calibrate (1961–1990) and validate (1991–2001) SDSM performance as well as to evaluate long-term trends in local climate conditions. Results indicate that SDSM provides better downscaling performance for temperature variables than for precipitation, with reliable reproduction of historical Tmax and Tmin patterns (NSE > 0.98). Downscaled future projections suggest consistent warming across the watershed, characterized by warmer winters and hotter summers, along with reduced temperature variability. Historical trend analysis reveals significant increases only in Tmin (0.033 °C per decade, p = 0.076), while Tmax and precipitation trends show weak or statistically insignificant changes. Future trends similarly indicate notable increases in Tmin, particularly during winter months (up to 0.13 °C per decade, p < 0.05), whereas changes in mean precipitation (Pmean) remain minimal. Increased summer precipitation variability, however, suggests a greater likelihood of heavy rainfall events, albeit with large uncertainty. Overall, this study demonstrates the utility of statistical downscaling for generating watershed-scale climate information and provides insight into evolving temperature and precipitation patterns that may influence environmental and resource management planning in southern Ontario.

Climate doi: 10.3390/cli14070149

Authors: Sujata Saunik Rajib Shaw

This study applies the Heat Resilience Framework (HRF) as a methodological approach to assess and compare heat resilience across Mumbai, Thane, and Nagpur. The HRF integrates multiple dimensions, namely Physical, Social, Economic, Institutional, and Environmental, to systematically evaluate vulnerabilities and adaptive capacities at the city level. Using the Analytical Hierarchy Process (AHP), indicators within each dimension were weighted and normalized to derive a comprehensive resilience ranking, allowing for a nuanced understanding of intra-city disparities and evidence-based policy interventions. The analysis reveals that Mumbai demonstrates the highest overall heat resilience, supported by strong physical, social, and institutional capacities, although economic resilience remains comparatively weaker. Thane exhibits relatively strong physical resilience but faces challenges in Institutional and Natural dimensions, contributing to a lower overall resilience score. Nagpur demonstrates the strongest economic resilience among the three cities but continues to face challenges in social resilience and adaptive capacity in the Natural dimension. These findings highlight the need for targeted interventions, including infrastructure improvements, financial inclusivity, strengthened institutional preparedness, and community-based resilience strategies, to address city-specific vulnerabilities. The study underscores the importance of adaptive measures such as green infrastructure, cool pavements, cooling shelters, and parametric insurance to enhance urban resilience. By leveraging the HRF, this study contributes to climate-responsive urban planning by offering a structured framework for policymakers to develop targeted, data-driven heat-mitigation strategies.

Climate doi: 10.3390/cli14070148

Authors: Julião da Costa Belo Tomás Calheiros Mário Gonzalez Pereira

Climate change poses significant environmental and socio-economic challenges for Small Island Developing States (SIDS), including Timor-Leste. This systematic review aimed to synthesise evidence on climate change impacts, vulnerabilities, adaptation pathways, and research gaps in Timor-Leste. Following PRISMA 2020 guidelines, literature searches were conducted in Web of Science, Scopus, and Google Scholar through May 2025. Eligible sources included peer-reviewed studies, technical reports, policy documents, and institutional publications. Source quality was assessed based on relevance, methodological consistency, credibility, and thematic contribution, and evidence was synthesised using a qualitative meta-synthesis approach. A total of 79 peer-reviewed studies and 8 international reports met the eligibility criteria, resulting in a final corpus of 87 documents. The evidence indicates a warming trend of 0.16 °C/decade and a sea-level rise of 5.5 mm/year with significant implications for agriculture, food security, water resources, and coastal systems. Approximately 70% of the population depends on climate-sensitive livelihoods, increasing exposure to climate-related risks. Evidence remains limited by data scarcity and methodological heterogeneity. Overall, Timor-Leste faces substantial climate vulnerability, highlighting the need for strengthened adaptation planning, improved climate information systems, and targeted policy interventions.

Climate doi: 10.3390/cli14070147

Authors: Anastasia V. Makhnykina Elena A. Kukavskaya Alexey V. Panov Anatoly S. Prokushkin Eugene A. Vaganov Pavel Ya. Groisman

Boreal forests are sensitive to external factors, and even small variations can completely alter their natural carbon (C) balance. Today, we face not only the consequences of climate change but also intensified human activity. In most cases, forest disturbances decrease ecosystem stability by disrupting key structural components, often transforming ecosystems into C sources. Soil CO2 efflux is a critical indicator of recovery following disturbances. This study explored the impact of different disturbance types (clear-cutting alone, wildfire alone, and both) on soil CO2 efflux for various ground-cover vegetation (lichens, green mosses, and Sphagnum mosses) in Scots pine forests of the Siberian central taiga. The data indicate that changes in soil CO2 efflux are predominantly driven by wildfires in lichen-dominated plots, by clear-cutting in green moss-dominated plots, and by both wildfires and clear-cutting in Sphagnum moss-dominated plots. Soil CO2 efflux was reduced by 75% and 40% in lichen- and Sphagnum moss-dominated plots, respectively, across burned sites compared to the undisturbed site. The impact of clear-cutting is observed in a decline of the emission rates of from 40 to 60% compared to the undisturbed site for lichen- and moss-dominated plots. The interactive effect of wildfire and logging is established in the reduction of the emission rates, and this decline depends on the type of ground cover. This finding highlights the substantial impact of wildfires and clear-cutting on the C dynamics of Siberian boreal ecosystems. The diurnal and seasonal variations in soil CO2 efflux were most pronounced for moss-dominated plots at disturbed sites. The main factors determining emission efflux variations are carbon content, type of ground cover, and forest floor thickness. This study contributes to a better quantitative understanding of soil CO2 efflux in boreal Scots pine forests from the differently disturbed areas.

Climate doi: 10.3390/cli14070146

Authors: Yaovi Aymar Bossa Jean Hounkpè

Reliable precipitation forecasts are critical for hydrological modelling and flood early warning in West African river basins, where rainfall is dominated by highly variable monsoon-driven convection. This study evaluates and improves the precipitation forecasting skill of six numerical weather prediction (NWP) models over the Ouémé River basin in Benin, with particular emphasis on lead-time dependence, basin-scale effects, and the added value of statistical bias correction. Daily precipitation forecasts, over the period 1985–2015 across lead times of one to seven days, are assessed across six sub-basins using complementary continuous and event-based verification metrics. The results indicate that precipitation forecast skill varies with model choice, forecast horizon, and spatial scale. Among the raw forecasts, the ECMWF and UK Met Office models consistently outperform the other systems with KGE values reaching 0.5. ECMWF exhibits the highest overall skill at short to medium lead times, while the UK Met Office model shows relatively low volumetric bias across most sub-basins (Pbias less than 25%). For some models, forecast performance improves with increasing basin size, reflecting the smoothing effect of spatial aggregation, although this relationship remains model-specific. Distribution-based methods outperform regression-based approaches, with empirical quantile mapping providing the most robust and consistent improvements across lead times and sub-basins. Following bias correction, Empirical quantile mapping achieved median Likelihood Ratio values of approximately 6 during validation, with upper-range values reaching 15–18 across sub-basins for both ECMWF and UK Met Office forecasts. This represents a substantial improvement over raw predictions whose distributions remained consistently bounded below 10 throughout the calibration and validation phases (more than 50% improvement). Overall, the combination of ECMWF or UK Met Office precipitation forecasts with empirical quantile mapping offers a reliable framework for improving precipitation inputs to hydrological models and flood early warning systems in the Ouémé basin. The findings highlight the importance of multi-criteria evaluation and appropriate bias correction when applying NWP precipitation forecasts in monsoon-influenced hydrological environments and flood forecasting.

Climate doi: 10.3390/cli14070145

Authors: Seno Basuki Wahyudi Hariyanto Forita Dyah Arianti Renie Oelviani Samijan Samijan Joko Triastono Joko Pramono Meinarti Norma Setiapermas Arnis Rachmadhani Lilam Kadarin Nuriyanto Dedi Sugandi Chanifah Chanifah Tri Martini Iwan Setiajie Anugrah Ansaar Ansaar Munir Eti Wulanjari Sri Minarsih Dewi Sahara R. Bambang Heryanto Yulis Hindarwati

The rainfed paddy farming system faces profound vulnerabilities due to daily climate non-stationarity. This mixed-methods study in Central Java analyses daily climate signals, total rice production, and household adaptation over 25 years (2001–2025). Moving beyond simple correlation, a Principal Component Regression model integrating five climate variables and three agronomic confounders reveals a profound climate–production decoupling. The composite climate index explains only 7.9% of total production variation, while non-climate factors account for 92.1%. Physical stability is maintained through asymmetric temporal scheduling and a distinct hierarchy of responses, employing active, planned adaptations alongside passive, reactive coping. However, quantitative household evaluation reveals this tonnage stability incurs severe hidden costs; the titip gabah post-harvest system maintains a high Yield Stability Index (0.93) but yields a negative Return on Storage (−7.15%), functioning as a risk-mitigation buffer rather than a profit-maximising tool. Furthermore, climate anomalies drive the progressive alienation of traditional ethnoclimatological knowledge, forcing a cognitive shift toward hybridised decision-making. To prevent passive coping from evolving into systemic maladaptation, we propose a stratified policy framework ranging from village-level knowledge integration and Subdistrict daily risk warnings to regency-level subsidies targeted at smallholders (<0.5 ha).

Climate doi: 10.3390/cli14070144

Authors: Gabriela Pinheiro Feitosa Eduardo Sávio Passos Rodrigues Martins Francisco das Chagas Vasconcelos Júnior Iago Alvarenga e Silva

Developing effective adaptation and mitigation strategies depends on climate predictions capable of representing future conditions across multiple temporal scales. Decadal climate predictions bridge seasonal forecasting and long-term climate projections, providing near-term climate information for decision-making and adaptation planning at multi-year timescales. This study assesses the predictive skill of CMIP6 decadal precipitation forecasts from the Decadal Climate Prediction Project for three strategic catchments in state of Ceará, in the Brazilian semi-arid region. Forecast skill was assessed using deterministic and probabilistic metrics for three averaging horizons corresponding to years 1, 1–5, and 1–10 after initialization. Systematic biases were assessed and corrected. The results indicate that predictive skill varies across forecast systems, averaging horizons, and catchments. While skill was generally lower for the 1–5-year averaging horizon, several forecast systems showed positive skill relative to climatology for the 1-year and the 1–10-year averaging horizons, especially for below-normal and above-normal precipitation categories. Although bias correction reduced effectively systematic errors, it did not consistently improve forecast skill. These findings suggest potentially useful predictive skill at decadal timescales and highlight the potential of decadal climate information to provide complementary information for near-term water resources planning and drought preparedness in the Brazilian semi-arid region.

Climate doi: 10.3390/cli14070143

Authors: Martina Zeleňáková Hany F. Abd-Elhamid Tatiana Soľáková Maria Manuela Portela Luis Angel Espinosa Jacek Barańczuk Katarzyna Barańczuk

Drought is becoming more frequent and severe in many regions, particularly in Mediterranean climates, where water demand is increased by warming and changes in precipitation regimes. A long-term assessment of meteorological drought at the Lisbon climatological station is provided in this study using the Standardized Precipitation Index (SPI) and the Reconnaissance Drought Index (RDI) over the period 1864–2021. Monthly precipitation and temperature data are used to compute SPI and RDI at 3-, 6-, and 12-month time scales, so that short-, mid-, and long-term droughts and their temporal evolution can be characterized. RDI is evaluated with three widely used empirical potential evapotranspiration (PET) formulations—Hargreaves, Thornthwaite, and Blaney–Criddle—in order to examine how PET estimations influence drought classification. Given the absence of a physically based reference PET—such as FAO-56 Penman–Monteith—for this station, the focus is on the internal consistency of the PET methods. Furthermore, the Hargreaves formulation is retained as a representative empirical PET for subsequent SPI–RDI comparison. The results show broadly consistent standardized RDI behavior across PET methods; it is indicated that drought conditions are captured more comprehensively by RDI than by SPI because both precipitation deficits and enhanced evaporative demand are included. At the Lisbon station, the estimated average return periods for short-, mid-, and long-term droughts are 3.79, 7.31, and 7.92 years according to RDI, compared with 3.86, 5.69 and 10.88 years from SPI. Several severe drought episodes are identified, including the years 1907, 1922–1923, 1944–1945, 1976, 1981, 1992–1993, 2005, and 2018. While no formal attribution analysis is performed, the drought characteristics are interpreted in the context of observed long-term warming and documented rainfall variability in Lisbon. The findings provide a single-station benchmark of historical drought behavior, by which local water-resources management can be supported and which can serve as a basis for future multi-station and climate-projection-based studies in Portugal.

Climate doi: 10.3390/cli14070142

Authors: Gamil Gamal Pavol Nejedlik Katarína Mikulová

While global climate change is fundamentally reshaping thermal regimes, capturing these shifts in topographically diverse regions remains a significant hurdle for standard gridded datasets. This study provides a comprehensive spatiotemporal analysis of 16 extreme temperature indices across Northern Carpathians from 1980 to 2024 using the E-OBS dataset. The QDM framework proved highly effective in neutralizing elevation-induced temperature biases, which reached up to 5.1 °C in raw E-OBS data. Beyond simple bias removal, the correction significantly improved the daily accuracy of the dataset, with RMSE values at high-altitude stations, such as the Chopok summit (1995 m), decreasing from 5.1 °C to 2.3 °C. Both Warm Days (TX90p) and Summer Days (SU) show near-perfect Field Coherence (Cf = 100% and 98%, respectively). A prominent feature of this temporal national average trend is its inherent asymmetry; the Annual Minimum (TNn) is climbing nearly twice as fast (+1.1 °C/decade) as the Annual Maximum (TXx) (+0.6 °C/decade), though the warming of these coldest nights is more localized (74.5% coherence). We also identified a clear signal of Elevation-Dependent Warming (EDW), with absolute maximums surging most aggressively in the Northern Carpathians at +1.6 °C/decade. Conversely, cold-tail indices like Ice Days are in a concurrent nationwide retreat (Cf = 97%), a shift that significantly reduces the physical window for winter tourism and alters the climatic envelope for fragile mountain ecosystems. Ultimately, these results position Slovakia as a high-sensitivity climate region where observed trends often outpace broader Central European averages, highlighting the urgent need for localized, nature-based adaptation strategies.

Climate doi: 10.3390/cli14070141

Authors: Todorka Samardzioska Milica Jovanoska-Mitrevska Slobodan B. Mickovski

Buildings account for a significant share of final energy consumption, with space heating representing one of the major energy uses in residential buildings. Therefore, improving the thermal performance of building envelopes is an important strategy for reducing energy demand. Green roofs can contribute to this objective by modifying roof thermal properties and reducing heat losses through the building envelope. This study investigates the use of machine learning to predict annual heating demand and potential heating energy savings associated with replacing conventional roof configurations with a selected green roof assembly in a representative stock of Macedonian buildings. A representative dataset comprising 2934 building cases based on post-2013 buildings designed in accordance with the national energy-performance regulations was assembled. The dataset covers a wide range of building typologies, envelope thermal properties, climatic conditions and heating schedules. Three supervised learning models, Random Forest, Artificial Neural Network and Extreme Gradient Boosting (XGBoost), were developed and compared. The results show that XGBoost achieved the highest predictive accuracy and the best computational efficiency, with test coefficients of determination of 0.9901 for the heating demand of conventional roof buildings and 0.9956 for green-roof-related heating energy savings. Most simulated buildings showed heating energy savings of up to 10% following green roof implementation, while only a limited number of cases exhibited increases in heating demand of up to 3%. The feature importance analysis identified heated floor area, heating duration and wall area as the major drivers of heating demand in conventional roof buildings, whereas roof thermal transmittance was the most influential factor governing green-roof-related heating energy savings. The findings demonstrate that machine learning can reliably reproduce the results of the established energy performance assessment methodology and provide rapid estimates of the potential heating energy savings associated with replacing conventional roofs with a selected green roof system across a representative building stock. The proposed approach can support engineers, urban planners and architects in the early-stage assessment of green roofs as an energy-efficient measure.

Climate doi: 10.3390/cli14070140

Authors: Ana Cruz-Baltuano Pablo Franco-León Nahuel Molero-Yañez David Alvarado-Kong Edgar Taya-Acosta Edwin Pino-Vargas

Agricultural drought represents a critical threat to water-dependent economies in arid Andean regions; however, forecasting tools tailored to data-scarce, high-altitude basins remain limited. This study developed and evaluated a multi-model spatiotemporal framework for agricultural drought forecasting in Candarave, in the northern Atacama Desert; we forecast the 3-month Standardized Precipitation–Evapotranspiration Index (SPEI-3) one month ahead (lead time t + 1) for an agricultural, data-scarce Andean basin. Seven modeling approaches were compared: three machine learning baselines (XGBoost, Random Forest, and Elastic Net), two statistical time-series models (ARIMA and ARIMAX), and two deep learning architectures (CNN-LSTM and ConvRNN). A driver analysis based on Elastic-Net coefficients identified spatiotemporal persistence (SPEI_neighbor, SPEI_lag1), precipitation, maximum temperature, and the Coastal El Niño Index (ICEN) as the dominant drought predictors. ARIMAX achieved the best overall performance (RMSE = 0.377; R2 = 0.909; NSE = 0.909; KGE = 0.889), demonstrating that incorporating exogenous climatic drivers substantially enhances forecasting skill. Among machine learning baselines, Elastic Net outperformed tree-based models (KGE = 0.924). Deep learning models revealed the weakest performance, with very low R2 values, attributed to insufficient training data, overparameterization, and the predominantly linear and persistence-driven nature of drought dynamics in Candarave. Under an operationally realistic configuration (all predictors lagged to forecast time), the approach provides a useful decision-support tool for one-month-ahead agricultural drought early warning, rather than a turnkey operational system.

Climate doi: 10.3390/cli14070139

Authors: Hadita Sapari Rohaida Ismail Wan Rozita Wan Mahiyudin Mohamad Ikhsan Selamat Mohamad Rodi Isa

Extreme temperatures are an emerging public health concern due to their significant impact on humans, yet the evidence remains limited in tropical countries. This study examined the non-linear relationship between ambient temperature and non-accidental and cause-specific mortality in two northern parts of Peninsular Malaysia, from 2011 to 2019. Daily mortality and meteorological data were analyzed using a quasi-Poisson Generalized Linear Model with a Distributed Lag-Non-Linear model to estimate the relationship between temperature and mortality. A U-shaped and J-shaped relationship was observed for the cumulative effects of 21-day lag periods for Kedah and Penang, respectively. The minimum mortality temperature (MMT) at 27.4 °C in Kedah and 28.2 °C in Penang was observed. Extremely high temperatures were associated with an increased non-accidental mortality, with a 16% increase at cumulative lag days 0–3 in Kedah and a 21% increase at cumulative lag days 0–7 in Penang. Vulnerable groups included individuals with respiratory diseases, the elderly, both genders and those residing in both urban and rural areas. These findings highlight the acute impact of heat on mortality in Malaysia and underscore the need for targeted public health interventions. Strengthening heat-health warning systems, improving healthcare preparedness, and prioritizing vulnerable populations are essential to mitigate the health impacts of rising temperatures in tropical regions.

Climate doi: 10.3390/cli14070138

Authors: Angelo Leogrande Carlo Drago Massimo Arnone Alberto Costantiello

HI35, defined as the mean annual number of days on which apparent temperature exceeds 35 °C, is introduced in this paper as a country-level heat exposure metric. Unlike vulnerability indicators, HI35 is treated as an exogenous climatic exposure variable and not as a direct measure of vulnerability or as an endogenous outcome. By combining HI35 with World Bank Environmental, Social, and Governance (ESG) indicators, this study applies unsupervised clustering algorithms to derive an exposure–vulnerability typology of countries. The Environmental, Social, and Governance pillars are analyzed separately through dedicated clustering procedures, supported by robustness checks and an additional country taxonomy based on principal component analysis and hierarchical clustering. The results identify heterogeneous country profiles in which similar levels of heat exposure coexist with different ESG-based vulnerability conditions, including environmental pressures, fragility, institutional capacity, innovation, water access, nutrition, and governance quality. Conversely, countries with relatively low heat exposure may display differentiated social or institutional vulnerability profiles. The empirical evidence suggests that heat exposure and ESG-based vulnerability are conceptually distinct but jointly relevant dimensions for classifying climate-risk profiles. No causal relationship is inferred between ESG indicators and HI35; the analysis is descriptive, classificatory, and based on unsupervised learning. Conceptually, HI35 captures the occurrence of extreme heat events, while ESG indicators describe the environmental, social, and institutional conditions associated with sensitivity, resilience, and adaptive capacity.

Climate doi: 10.3390/cli14070137

Authors: Lian Xie

This study evaluates the 20-year operational performance (2006–2025) of a preseason prediction system for Atlantic hurricane activity developed at North Carolina State University (NCSU) and compares it with forecasts from Colorado State University (CSU), Tropical Storm Risk (TSR), and NOAA. Unlike previous studies based primarily on hindcast experiments, this analysis uses real-time forecasts generated under evolving model configurations, providing a realistic assessment of operational forecast skill. Results show that NCSU April forecasts exhibit lower mean absolute error than other April-issued forecasts and achieve performance comparable to later-issued forecasts from NOAA and CSU, indicating that improved model formulation can partially offset the advantage of later initialization. To identify the sources of forecast improvement, regression and ensemble analyses are conducted. Forecast adjustments between early- and late-season forecasts are primarily explained by changes in tropical North Atlantic sea surface temperature (SST), while ENSO contributes secondarily as forecast uncertainty decreases beyond the spring predictability barrier. These results establish a clear hierarchy of predictors, with Atlantic SST providing the dominant source of preseason predictability. Multi-model ensemble experiments further show that simple averaging does not outperform the best individual models; instead, selective combinations yield the highest skill, with optimal configurations differing between named storm and hurricane predictions, demonstrating that forecast improvement depends on combining complementary information rather than increasing ensemble size. Forecast performance is also shown to be predictand-dependent, with named storm counts more sensitive to late-spring environmental evolution and hurricane counts more strongly constrained by basin-scale thermodynamic conditions. Despite these advances, all models exhibit reduced skill during extreme seasons, reflecting the intrinsic limits of seasonal predictability. Overall, these results demonstrate that preseason hurricane forecast skill is governed by the interaction of forecast timing, model capability, and a hierarchical structure of environmental predictors, providing a unified framework for interpreting differences among forecasting systems and guiding future model development.

Climate doi: 10.3390/cli14070136

Authors: Li Liu Chuhan Lu Julong Huang Feng Zhang Guangyu Qu Lu Guo Runze Luo

Traditional meteorological data spatial interpolation methods often rely on linear or static assumptions, which are inadequate for complex terrain and fail to exploit continuous spatiotemporal variation information. This paper proposes a Spatiotemporal Graph Network with Adaptive Temporal Decay (DG) that integrates a learnable graph convolution module and a temporal attenuation mechanism, enabling accurate precipitation estimation for target stations or regions at consecutive time steps. The method is evaluated using daily precipitation data from nine stations in Longnan City, Gansu Province, China, along with ERA5 (0.25°) and GPCP (0.5°) gridded reanalysis products. In the station-to-station interpolation scenario, DG significantly outperforms ordinary Kriging (OK), reducing the average RMSE from 1.4 mm/day to 1.2 mm/day, with a 28.6% improvement at mountainous stations. The DG model also exhibits superior performance in grid-to-station interpolation, achieving an average RMSE of 1.9 mm/day (OK: 2.5 mm/day). On heavy precipitation days (≥20 mm/day), DG reduces the RMSE nearly by half (11.7 mm/day) compared to OK (23.2 mm/day). A temporal-only LSTM baseline and three ablation variants (spatial-only OSI, temporal-only OTI and dgcn-only OD) are also compared, and DG consistently outperforms them, confirming the essential role of spatiotemporal integration. Additional baselines including IDW and Co-Kriging further validate the superiority of DG. The proposed method offers a promising new approach for high-precision spatiotemporal interpolation of meteorological elements in complex terrain.

Climate doi: 10.3390/cli14070135

Authors: Coskun Firat Asfaw Beyene

Urban heat stress is intensifying under climate change, particularly in outdoor public spaces where conventional mechanical cooling is impractical. This study develops a climate-driven, system-level numerical framework to evaluate the pre-deployment feasibility of modular, solar-powered spray cooling canopies across 110 cities in Türkiye. Hourly Typical Meteorological Year (TMYx) weather files, representing a single typical year constructed from 2009 to 2023 source data, are used to estimate photovoltaic (PV) energy yield, electrical load, feasible misting duration, water demand, and PV-to-load autonomy under summer daytime conditions. The misting operation is governed by a rule-based adaptive control strategy based on air temperature, relative humidity, and plane-of-array irradiance. To support transferable comparison, the cities are classified into six summer climate-archetype zones using k-means clustering of standardized climate variables, including temperature, humidity, irradiance, wind speed, and summer precipitation. Results show that evaporative cooling feasibility is governed primarily by humidity rather than temperature alone. Hot–Dry Inland cities exhibit the longest mean misting duration (501.90 h) and highest water demand (30,152 L per module), but the lowest PV-to-load autonomy ratio (1.55) because of high pump-driven electrical demand. In contrast, Humid Black Sea cities show minimal misting duration (11.43 h) and water use (465 L per module), but the highest autonomy ratio (39.68) due to very limited system activation. Thus, high autonomy does not necessarily indicate high cooling usefulness. The proposed framework provides a reproducible screening tool for identifying where PV-powered spray cooling canopies are climatically suitable, where water and PV sizing become limiting, and where alternative outdoor heat-mitigation strategies may be more appropriate.

Climate doi: 10.3390/cli14070134

Authors: Johanna Yepes Juliana Valencia Alejandro Builes-Jaramillo

Convectively Coupled Kelvin Waves (CCKWs) play a key role in synoptic variability and can trigger extreme hydrometeorological events. This study characterizes the influence of CCKWs on seasonal precipitation patterns and extreme precipitation events (EPEs) over northern South America. Using a filtered OLR dataset, we found that precipitation anomalies associated with CCKWs are sensitive to the selected index region. A sensitivity analysis identified a region in the Colombian Pacific exhibiting the strongest precipitation anomalies linked to CCKWs. At seasonal scales, March–May (MAM) is the season with the highest CCKW activity, and its convective phase is associated with enhanced precipitation over the far eastern Pacific, western Amazonia, and northern Colombia, while suppressed convection dominates northwestern Brazil. In addition, three regions exhibit increases of up to 30% in EPE occurrence during convective-phase Kelvin waves: (i) the northwestern Amazon, (ii) northwestern Colombia, and (iii) the Peruvian coast. In contrast, EPE occurrence in the Colombian Pacific appears largely independent of CCKW passage, likely due to the strong background climatological rainfall in the region. We also analyze a flooding event in Turbo, Colombia, on 9 May 2007, that occurred during the passage of a convective-phase Kelvin wave and was preceded by days of enhanced low-level southwesterly flow convergence and persistent rainfall. Understanding the influence of these intraseasonal oscillations on precipitation and EPEs is essential for improving regional weather forecasts and supporting the development of early warning systems.

Climate doi: 10.3390/cli14070133

Authors: Ivan Bešlić Suzana Sopčić Zdravka Sever Štrukil Domagoj Mihajlović

The mixing layer height (MLH) is a key meteorological parameter governing the dispersion and accumulation of air pollutants in the atmospheric boundary layer. Over the Zagreb urban area, low MLH values are associated with stable atmospheric conditions and temperature inversions that inhibit vertical air mixing and promote pollutant accumulation near the ground. This study investigates the influence of MLH on concentrations of particulate matter with an aerodynamic diameter smaller than 10 µm (PM10) and particle-bound polycyclic aromatic hydrocarbons (PAHs) using a five-year dataset of radiosonde measurements and air quality observations. Daily MLH values at 12:00 UTC were compared with simultaneous measurements of PM10 and PAH concentrations. Inversion conditions (MLH < 1000 m) occurred on 44% of days, with approximately 80% of events recorded during the cold season. Elevated PM10 and PAH concentrations were strongly associated with these stable conditions. Of 69 daily exceedances of the PM10 limit value (50 µg m−3), 62 occurred during inversion events. Statistical tests confirmed significantly higher concentrations during stable atmospheric conditions (p < 0.05), with further increases observed during prolonged inversion periods. Empirical relationships between MLH and pollutant concentrations were developed and may support future machine-learning-based air quality forecasting. The results highlight atmospheric stability as a critical factor for urban air quality management.

Climate doi: 10.3390/cli14060132

Authors: Maria F. Arriaga-Osuna Karen E. Martínez-Torres Marcos E. Gonzalez-Trevizo Carlos J. Esparza-Lopez Brenda Y. González-López

The rapid expansion of urbanization in recent decades has intensified the urban heat island effect, driven by reduced vegetation cover, widespread use of heat-absorbing materials, and increases in surface and atmospheric temperature that may reach 5–6 °C. These conditions negatively impact well-being, quality of life, and human health. In response, numerous studies have examined mitigation strategies based on high-albedo materials and urban vegetation. This systematic review analyzes 225 peer-reviewed articles published between 2016 and 2025 addressing urban heat mitigation, surface thermal conditions, urban vegetation, outdoor thermal comfort and microclimate simulations. It provides a comprehensive synthesis, highlighting key findings and implications for future research. According to the Köppen–Geiger classification, most studies were conducted in humid subtropical and warm Mediterranean climates. The analysis focuses on urban canyon interventions, where vegetation is primarily modeled as shading trees (79.2%), along with other forms such as grass or shrubs (27.1%), mainly during the summer season. Results indicate that integrated mitigation strategies combining vegetation and high-albedo surfaces (≈0.8) generally provide greater cooling benefits than isolated interventions. Overall, the findings underscore the importance of the interaction between vegetation shading and surface properties for mitigating urban heat in outdoor spaces.

Climate doi: 10.3390/cli14060131

Authors: Tyler Buchanan Kristofor Brye Diego Della Lunga Will Dockery Mike Daniels Samantha Robinson Bronc Finch

Grazing lands are foundational for the United States (US) livestock industry. In Arkansas, pastures are essential for rotational grazing and dairy operations. Climate change is an increasing concern in agriculture due to anthropogenic activities promoting greenhouse gas (GHG) emissions, partly due to nutrient recycling that occurs from animal manure additions. The objective of this study was to quantify and evaluate the potential effects of grazing method (i.e., enhanced grazed (EG) and minimally grazed (MG))on carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) fluxes, season-long emissions, and global warming potential (GWP) over two consecutive growing seasons (i.e., 2024 and 2025) in the Ozark Highlands region of northwest Arkansas. In 2024, averaged over time, the CO2 flux from the EG (880 mg m−2 h−1) was greater (p ≤ 0.05) than from the MG (687 mg m−2 h−1) treatment. Averaged across grazing treatment, season-long CO2 emissions and GWP were at least 1.8 times greater (p ≤ 0.05) in 2025 than 2024, while season-long CH4 emissions were 4.6 times greater (p ≤ 0.05) in 2024 than 2025. Averaged across year, season-long N2O emissions were greater (p ≤ 0.05) from the EG (1.6 kg ha−1) than from the MG (0.38 kg ha−1) treatment. Two-year-cumulative, season-long CH4 and N2O emissions and GWP from only CH4 and N2O were greater (p ≤ 0.05) in the EG compared to the MG treatment. Considering the large land area devoted to various agricultural grazing operations throughout the US, understanding the magnitude of GHG emissions from different grazing strategies will contribute to improving GHG mitigation efforts in managed grazing lands.

Climate doi: 10.3390/cli14060130

Authors: Ante Bubalo Irena Jug Helena Žalac Vladimir Ivezić Goran Herman Irena Ištoka Otković Mirna Habuda-Stanić Brigita Popović

Extreme weather conditions and greenhouse gas emissions cause increased pressure on modern agriculture. To ensure long-term resilience, agricultural production requires sustainable and integrated production systems. Agroforestry offers an effective approach by increasing soil organic carbon, improving carbon sequestration, and reducing greenhouse gas emissions. When combined with other sustainable practices, these systems can further strengthen the resilience and sustainability of agriculture. However, despite these advantages, agroforestry systems are not without challenges, as they require higher initial investments, greater knowledge and labor input, and longer periods to achieve economic efficiency. This paper presents data on the effects of agroforestry systems on different aspects of agricultural production, highlighting their opportunities and limitations.

Climate doi: 10.3390/cli14060129

Authors: Umberto Triacca Antonello Pasini

Recent studies have investigated whether the rate of global warming has changed since the 1970s, with particular attention to the role of natural variability and its removal from temperature time series. In particular, Foster and Rahmstorf analyzed global mean surface temperature series, adjusted for natural variability. However, their procedure might produce spurious changepoints, since it does not appropriately handle the autocorrelation present in the residuals of the models considered. In this study, we revisit the same adjusted temperature series using a different methodology (the Quandt likelihood ratio test) while properly accounting for the presence of autocorrelation. We find evidence that global temperature has departed from its previous path since around 2013–2014. Our results provide robust proof of a clear recent increase in the temperature trend for adjusted time series, at a rate of warming that has doubled since that date.

Climate doi: 10.3390/cli14060128

Authors: Daniel Schuch Kiarash Farzad Yang Zhang

Air pollution poses significant risks to public health, ecosystems, and regional economies, particularly in rapidly developing regions. Despite its importance, the Middle East remains relatively understudied in regional air quality, with limited evaluations of pollutant transport and model performance. This study applies the WRF (Weather Research and Forecasting) model coupled with the CAMx (Comprehensive Air Quality Model with Extensions) model to simulate meteorology and air quality over West Asia, with a focus on the United Arab Emirates (UAE). Six representative months are analyzed, including three winter periods (January 2018, 2020, 2022) and three summer periods (June 2017, 2019, 2021). WRF shows good agreement with observations, reproducing near-surface temperature with an index of agreement (IOA) between 0.90 and 1.00 and generally low wind speed (MB < ±0.5 m s−1) and wind direction biases (MB < ±0.5), although cloud-radiative forcing is underestimated during winter. CAMx reproduces PM2.5 concentrations with moderate-to-high correlations (r = 0.44–0.65) and low bias, while AOD and O3 column concentration show larger uncertainties. Satellite-based evaluation indicates good performance for NO2 and CO column abundances but larger discrepancies for HCHO and SO2, particularly during summer. Overall, the results demonstrate that the WRF-CAMx modeling system provides a reliable framework for regional air quality simulations over West Asia, while highlighting uncertainties associated with emissions, atmospheric chemistry, and satellite retrieval products.

Climate doi: 10.3390/cli14060127

Authors: Natalia G. Vallianou Eleni V. Geladari Vasileios Sevastianos Maria Masouridi Andreas Adamou Nikos Adamidis Fotis Panagopoulos Alexandros Tousis Ilektra Tzivaki Dimitris C. Kounatidis

Dengue virus infection is a febrile illness caused by the Orthoflavivirus Dengue, which is transmitted by the mosquitoes Aedes aegypti or Aedes albopictus. Despite the fact that Dengue virus (DENV) is present in tropical and subtropical areas, climate change with global warming has been associated with the spread of Aedes aegypti and Aedes albopictus mosquitoes in several other regions worldwide. Notably, as the presence of Aedes albopictus has been confirmed in Southern Europe, already locally transmitted cases of Dengue virus infection have been reported in Europe. Apart from Europe, Australia has reported DENV cases in the 21st century that have been associated with the transmission of Aedes aegypti in the neighboring islands. Climate change, namely increasing temperatures, higher humidity and rainfalls, together with the development of urban heat islands, uncontrollable deforestation and urbanization, travelling and trade, has contributed significantly to the spread of DENV infection. Modern diagnosis based upon the advent of “multi-omics” techniques and machinery learning programs will be of the utmost importance for the early and accurate diagnosis of DENV infection. Finally, preventive measures for controlling Dengue virus infection, such as the use of repellents, educational programs, and improvement in water storage and waste management at the community levels would be very useful. Regarding climate change, the One Health Approach by integrating collaboration of various sectors and raising public awareness seems to be of the utmost importance in this context. Further investigations regarding the development of antiviral agents and vaccines will be an important asset in our armamentarium against DENV infection.

Climate doi: 10.3390/cli14060126

Authors: Niloofar Mohammadi Raoof Mostafazadeh

Human-induced climate change has rendered urban areas highly vulnerable to extreme events such as heatwaves, droughts, and floods. This study conducts a scoping review of extreme and compound climate hazards in Iranian urban areas under global warming conditions. Mapping the available literature, 92 authoritative scientific works published between 1999 and 2025 were analyzed. The review synthesizes evidence on the spatiotemporal patterns of heatwaves, drought, torrential rainfall, sea-level rise, and compound hazards across Iran. The results indicate that central, northwestern, eastern, and southern Iran experience the highest heatwave intensity and frequency, with short-duration heatwaves being more common than prolonged ones. Western Iran faces a high risk of torrential rainfall, but urbanization amplifies flood consequences by expanding impervious surfaces and accelerating surface runoff. Coastal areas show high vulnerability to compound flooding due to sea-level rise and storms. The review further reveals that Iran is experiencing hydroclimate whiplash (abrupt transitions between drought and flood) driven by global warming. The study concludes by presenting management suggestions and future research directions for integrated compound hazard management in Iran.

Climate doi: 10.3390/cli14060124

Authors: Hala Rossi El Khalil Cherif El Mustapha Azzirgue Hamza El Azhari Hakim Boulaassal Omar El Kharki

Climate change and increasing water scarcity are intensifying pressure on irrigated agriculture, which currently represents 70% of global freshwater withdrawals. Remote sensing technologies have become essential tools for monitoring soil moisture, evapotranspiration, crop growth, and irrigation performance across multiple spatial and temporal levels. This review synthesizes 83 peer-reviewed studies published between 2002 and 2025, focusing on the use of optical, thermal, and microwave sensors to support irrigation water management under climate variability. The analysis highlights progress in multi-sensor integration, UAV-based monitoring, crop and agro-hydrological modeling, and emerging machine learning approaches that enhance irrigation scheduling, soil moisture estimation, and crop water stress detection. Despite these advancements, several methodological challenges persist, including data integration constraints, sensor-specific limitations, model transferability issues, insufficient ground validation, and difficulties in translating remote sensing outputs into operational decision support systems. In addition, structural gaps at the policy level restrict the evaluation of irrigation efficiency and climate resilience. This review aims to clarify current limitations and outline priority research directions to enhance the climate resilience and sustainability of irrigated agricultural systems.

Climate doi: 10.3390/cli14060125

Authors: Shuaiyi Shi Paul I. Palmer Fei Yao

Aerosols emitted by biomass burning represent one of the largest sources of uncertainty in our current understanding of the Earth’s radiative balance. We investigate the climatic influence of biomass burning aerosols emitted from six key regions of biomass burning by using GEOS-Chem coupled with the rapid radiative transfer model. We evaluate our model using AERONET observation, with the model reproducing data with 87% observed spatial and seasonal variability with a low negative bias of 7%. The radiation sensitivity is generally highest for North Asia (NAS) and for North America (NCC); lowest for South America (SAM) and South and Southeast Asia (SSA); and moderate for Africa (AFR) and Oceania (OCE). These regional differences are related to the main burning types of the regions. When we consider the global radiation influence, AFR dominates the global picture due to the comparatively large biomass burned. We estimate the global mean radiation influence of biomass burning aerosol is −0.116 W m−2. For monthly features, in summer, due to higher incident energy obtained in NAS and NCC, high negative radiation sensitivity of biomass burning, biomass burning aerosols, and biomass burning organic aerosol are shown in these regions. Meanwhile, the radiation sensitivity peak of black carbon for these two regions occurs earlier in late spring (NAS) or early summer (NCC), when large incident energy and large high reflectance snow cover coexist in these two high-latitude regions. A significant yearly difference in radiation influence, rather than radiation sensitivity, is found, with the relative difference between the maximum year and minimum year reaching 90% of the maximum radiation influence year. Specifically, two regions affected by El Niño (OCE and SSA) have the most significant yearly variation in all factors, with anomalies occurring in El Niño years.

Climate doi: 10.3390/cli14060123

Authors: Abhilash Hemareddy Thimmareddy Iyyappan Jaisankar Arkadeb Mukhopadhyay Gurunath Raddy

Climate change directly affects agricultural productivity, particularly in small island systems where ecosystems and livelihoods are highly exposed to climate variability. This study presents a comprehensive analysis of climate variability for the three districts North and Middle Andaman, South Andaman, and Nicobar, using a six-model CMIP6 ensemble under four SSP scenarios (SSP126, SSP245, SSP370, and SSP585), coupled with ensemble tree-based machine learning algorithms to project coconut yield responses. The historical data was analysed from 1981 to 2025 and the projection was from 2026 to 2100. Observed rainfall reveals a persistent north-to-south gradient, with South Andaman recording the highest mean annual rainfall (3408.40 mm) and Nicobar recording the lowest (2442.13 mm), alongside pronounced inter-annual variability and a discernible drying tendency post-2015. Nicobar consistently records the warmest mean Tmax (30.89 °C) and Tmin (24.11 °C), while North and Middle Andaman exhibit the greatest inter-annual temperature variability. Future projections indicate a robust and statistically significant warming across all districts and scenarios, with end-of-century Tmax increases reaching up to 4.05 °C (Nicobar, SSP585) and Tmin increases up to 3.73 °C (North and Middle Andaman, SSP585), accompanied by a progressive compression of the diurnal temperature range. Precipitation projections show modest wetting in the Andaman districts under most scenarios, while Nicobar exhibits a muted response, with SSP370 uniquely projecting a decline of approximately 69 mm below the observed baseline. Among the ten evaluated CMIP6 models, six (ACCESS-CM2, CMCC-ESM2, CNRM-ESM2-1, EC-Earth3-Veg-LR, GFDL-ESM4, and NorESM2-MM) were selected based on composite skill scores across rainfall, Tmax, and Tmin. Model selection was optimized independently for each district via Leave-One-Year-Out cross-validation with hyperparameter tuning, yielding district-specific best performers: GradientBoost for North and Middle Andaman (R2 = 0.471), RandomForest for South Andaman (R2 = 0.609), and ExtraTrees for Nicobar (R2 = 0.289). K-Nearest Neighbours demonstrated competitive predictive skill in all three districts, confirming that instance-based learning can capture non-linear climate–yield relationships, though tree-based ensembles were preferred for their robustness and interpretability. Ensemble tree-based ML models and instance-based learning consistently outperformed all linear and kernel-based approaches, confirming the non-linear nature of climate–yield relationships in this setting. Coconut yield projections indicate above-baseline productivity gains of 3.4–21.5% in North and Middle Andaman and 24.6–36.8% in South Andaman, driven by favourable warming and precipitation trends, while Nicobar yields plateau at 7.7–13.7% above baseline, indicating thermal saturation of the climate yield response under already near-optimal thermal conditions. Notably, Nicobar exhibits a reversed yield–emission relationship wherein lower-emission pathways marginally outperform high-emission scenarios, likely reflecting avoidance of thermal stress thresholds. Inter-CMIP6-model uncertainty emerges as the dominant source of projection spread, exceeding scenario uncertainty across most districts, underscoring the critical importance of multi-model ensemble frameworks for robust agricultural climate impact assessments in data-sparse tropical island environments.

Climate doi: 10.3390/cli14060122

Authors: Daryl Stephens Saraniya Tharmarajah Valicia Browne Graham Sack Wonjung Bae Rajiv N. Rimal

Despite overwhelming scientific consensus that human activity drives climate change, public opinion in the United States remains sharply polarized along political lines. This project tests whether a theory-driven narrative intervention can reduce divergence between individuals skeptical of climate change and those who accept the scientific consensus. Guided by narrative transportation theory, we hypothesize that an inclusive, character-driven video grounded in the authentic language of skeptical audiences will reduce polarization and increase civic engagement. The study proceeds in three phases. Phase 1 uses focus group discussions to identify words, phrases, and perspectives used by skeptical and accepting participants. Phase 2 integrates these findings into the production of a 2–3 min narrative short film, refined through iterative audience testing. Phase 3 employs a stratified online experiment assessing climate attitudes, policy support, and activism behaviors before exposure, immediately after, and one week later. Mediators include narrative transportation, perceived similarity, and character identification. We test whether pre-exposure divergence narrows over time and whether engagement mechanisms explain observed changes. Findings will inform climate communication policy, intervention design, and broader research on depolarization in polarized public issues.

Climate doi: 10.3390/cli14060121

Authors: Chen Liang Zac Flamig James P. Kossin Edward J. Kearns

Assessing drought risk under evolving climate conditions is critical for adaptation planning, yet it remains challenged by projection uncertainty and methodological complexity. This study presents a global probabilistic framework for estimating self-calibrating Palmer Drought Severity Index (scPDSI) drought return periods by integrating observational climate data with statistical modeling. We combined the scPDSI with a stochastic weather generator and generalized extreme value (GEV) analysis to evaluate drought duration extremes at a 2.5° × 2.5° global resolution. The weather generator creates 1000 synthetic time series per grid cell to enable probabilistic assessment, reproducing observed variability, persistence, and long-term trends. To project future risk, we derived return periods by scaling synthetic series using regional temperature change factors from a multi-model CMIP6 ensemble. Results indicate broad agreement with climate model ensembles but highlight regions where nonlinear dynamics drive divergences. We explicitly address critical methodological limitations raised in the recent literature, including the use of scPDSI as a sole indicator, the assumption of stationary variance in the stochastic generator, and the statistical challenges of modeling discrete drought durations with GEV distributions. This framework offers a spatially explicit, observationally grounded tool for decision-makers, while underscoring the necessity of multi-index validation in future global assessments.

Climate doi: 10.3390/cli14060120

Authors: Mohammad Darwish Shatha Elnakib Osama Ali Maher Catello M. Panu Napodano Saverio Bellizzi

Climate change represents one of the defining global health challenges of the 21st century, with far-reaching implications for population health, health systems, and health equity. The acceleration of environmental change, evidenced by record-breaking global temperatures, extreme weather events, and ecological degradation, poses a direct threat to achieving Sustainable Development Goal 3 (SDG 3), which aims to ensure healthy lives and promote well-being for all. This manuscript presents a narrative review and policy analysis of the intersection of climate change and global public health in light of the outcomes of the 2025 United Nations Climate Change Conference (COP30) in Belém, Brazil. Drawing on peer-reviewed literature, major institutional reports, and relevant policy documents, we explore how climate change exacerbates communicable and non-communicable diseases, undermines health system resilience, and disproportionately affects vulnerable populations worldwide. Particular attention is given to heat-related morbidity, infectious disease expansion, air pollution, food and water insecurity, displacement, gender inequities, antimicrobial resistance, and mental health impacts. The paper highlights the significance of the Belém Health Action Plan (BHAP), which is treated here as a COP30-associated action framework that places health more centrally within climate policy discussions. However, major challenges remain, including its voluntary orientation, the absence of dedicated financing mechanisms within the framework itself, and limited clarity on accountability arrangements, as identified through our synthesis of the available policy and evidence base. We argue that achieving SDG 3 is no longer feasible without integrating climate adaptation and mitigation into health systems and policies, and that progress will depend on translating global commitments into context-specific country strategies, governance arrangements, and implementation pathways.

Climate doi: 10.3390/cli14060119

Authors: Kouchrad Ikhlass Janah Nada Odgou Mohammed

Climate resilience has evolved and transitioned from a concept focused on disaster risk to a strategic development paradigm. It has become a core area of focus for researchers, professionals, and policymakers due to the increasing frequency and severity of climate change hazards. The academic landscape persists in a fragmented state in spite of its significant prominence due to diverse conceptual frameworks, various definitions, and a lack of precise indicators to assess climate resilience across sectors. The crucial objective of this research is to conduct a comprehensive bibliometric analysis of the academic literature on climate resilience, measure the scientific influence, and identify gaps and opportunities. This bibliometric review was conducted using data from Web of Science, consisting of 1096 articles published between 2015 and 2025. Vosviewer represents the main software used to evaluate the network of leading authors, journals, international collaborations, and the dominant countries. Terms such as climate change, resilience, and indicators received particular attention, representing the main conceptual connections. This study reveals an overview of the field’s progression, themes, trends, and challenges. The results reveal a sustained increase in research output and a heterogeneous landscape organized around key domains, including urban resilience, ecosystem dynamics, agricultural systems, governance, climate impacts, and sustainability transitions. Resilience is assessed using diverse, context-specific indicators, with governance, vulnerability, and adaptive capacity frequently identified as core dimensions. However, measurement approaches remain inconsistent and lack standardization. Scientific production is concentrated in a limited number of countries, although international collaboration is gradually expanding. These findings underscore the multidimensional and evolving characteristics of climate resilience research, with no clear movement toward a unified measurement framework.

Climate doi: 10.3390/cli14060118

Authors: Ridouane Kessabi Mohamed Hanchane Nir Y. Krakauer Mohamed Belmahi

The upper Sebou River occupies a strategic territory draining varied mountain reaches in northern Morocco. As such, it is rich in surface water resources and karst springs with important downstream uses. However, the variability of rainfall threatens its water potential, making it highly vulnerable and at risk of desiccation. This study explores rainfall trends and their effects on streamflow and water resource availability. Data from three stations representing the upstream section of the watershed, along with two streamflow series—one for the upper Sebou River (Pont Medz) and the other for the Aïn Timdrine karst spring—cover the period from 1956 to 2018. The methodology employs Mann–Kendall trend tests, Sen’s Slope test, and the Standardized Precipitation Index (SPI) for rainfall series, as well as the Streamflow Drought Index (SDI) for hydrological series. The results demonstrate a decline in rainfall since 1979, significant at the 5% threshold. This trend has an immediate impact on the flow rates of the area’s rivers and karst springs, which have also tended to decline, with a succession of dry years and seasons since 1980. This observation highlights the depletion of water resources of the fragile upper Sebou region in the face of decreasing rainfall and snowfall, compounded by the rampant and unsustainable exploitation of groundwater resources linked to the development of irrigated cash crops in the Middle Atlas Mountains.

Climate doi: 10.3390/cli14060117

Authors: Alejandro Ordoñez-Sánchez Martín José Montero-Martínez Mercedes Andrade-Velázquez Gabriela Colorado-Ruíz Tereza Cavazos

Reliable precipitation projections are essential for water-resource management, flood-risk assessment, and drought preparedness in hydroclimatically complex regions such as Mexico, where uncertainty remains high due to monsoon dynamics, complex topography, and tropical moisture transport. This study evaluates paired CMIP5 and CMIP6 global climate models in simulating the historical (1940–2005) precipitation annual cycle across four regions of Mexico (NW, NE, SW, SE). Model outputs were compared against ERA5 and cross-validated with CRU using complementary metrics assessing error magnitude, variability, temporal phase, and spatial coherence. Results indicate that CMIP6 provides moderate but regionally heterogeneous improvements rather than a uniform advance. The most consistent gains occur in NE and SE Mexico, where dry biases are reduced and seasonal amplitude is better represented. In contrast, SW Mexico exhibits persistent summer wet biases linked to monsoon–topography interactions, while improvements in NW Mexico are mainly confined to selected individual CMIP6 models and are not consistently reflected in the ensemble median. A marked SW–SE summer dipole bias highlights ongoing deficiencies in representing moisture transport and convection. These findings demonstrate that increased model complexity does not guarantee improved regional skill and that ensemble medians may mask individual model performance, underscoring the need for targeted model selection, multi-dataset validation, and bias-correction strategies.

Climate doi: 10.3390/cli14060116

Authors: Daniel Schuch Yang Zhang

Building on the regional model evaluation presented in Part I, this study investigates the processes controlling air pollutant formation and transport over West Asia, with a focus on the United Arab Emirates (UAE). Two representative months, January 2022 and June 2021, are selected for detailed analysis using Chemical Process Analysis (CPA) and Integrated Process Rate (IPR) diagnostics. The results indicate predominantly VOC-limited ozone (O3) formation across urban and coastal regions, with seasonal and spatial transitions toward NOx-limited regimes, particularly in rural and downwind areas. IPR diagnostics show that local chemistry and vertical transport are the dominant contributors to O3 variability, whereas fine particulate matter with a diameter of 2.5 µm or less (PM2.5) variability is primarily driven by vertical transport and emissions. Horizontal transport and land–sea circulation play an important role in shaping the spatial distribution of both pollutants, especially along coastal zones. Comparisons among urban, coastal, and rural sites further highlight the influence of topography, land use, and meteorological conditions on pollutant dynamics. These process-based insights provide a scientific basis for refining emission control strategies, improving regional air quality management, and supporting evidence-based policies to mitigate air pollution impacts on human health and the environment in West Asia.

Climate doi: 10.3390/cli14060115

Authors: Damir Ugarković Ivana Medved Irena Šapić Kristijan Maričić Roman Rosavec

This study analysed climate change in the eu-Mediterranean and sub-Mediterranean areas of the Istrian Peninsula, Croatia, and examined the influence of climatic conditions on crown defoliation in pubescent oak and Aleppo pine. Climate data from representative meteorological stations for the period 1980–2022 and crown defoliation data for 2000–2023 were analysed. Drought conditions were assessed using potential evapotranspiration, rainfall anomaly indices, and the number of dry months. The results indicated a significant increase in mean annual air temperature and potential evapotranspiration throughout the Istrian region, with temperatures rising by 0.8–1.2 °C compared to the reference period. Southern Istria experienced three dry months, characteristic of the steno-Mediterranean vegetation zone, indicating increasing aridity. The highest proportion of trees was recorded in the 26–60% defoliation class, including 54% of Aleppo pine and 59% of pubescent oak trees. Crown defoliation showed significant correlations with precipitation, rainfall anomaly index, and air temperature. Higher temperatures were associated with moderate defoliation levels, while lower minimum temperatures negatively affected severely defoliated Aleppo pine trees. No statistically significant differences in crown defoliation were found between pubescent oak and Aleppo pine.

Climate doi: 10.3390/cli14060114

Authors: Claudia Ballesteros-Barrera Oscar Tapia-Pérez Adrián Leyte-Manrique Angélica Martínez-Bernal Rocío Zárate-Hernández Bárbara Vargas-Miranda Matías Martínez-Coronel Selene Ortiz-Burgos

Climate change threatens global biodiversity, with amphibians from climatically stable and geographically restricted ecosystems such as Mexico’s montane cloud forest (CF) being particularly vulnerable. This study evaluated the potential impacts of climate scenarios on the distribution and richness of 53 endemic anuran species. We used ecological niche models (MaxEnt) to project current and future distributions (year 2100) under the SSP2-4.5 and SSP5-8.5 scenarios, and assessed species representativeness within federal Protected Natural Areas (PNAs). The results indicate that 71.7% of species already fall into an IUCN threat category. Widespread habitat contraction is observed under the climate projections, with average losses of 40.3% (SSP2-4.5) and 45.5% (SSP5-8.5). Twelve species (22.6%) could lose over 90% of their current distribution, suggesting a high risk of functional extinction. Only 15.3% of occurrence records currently fall within PNAs, and key reserves such as Los Tuxtlas and La Sepultura are projected to experience significant richness declines. These patterns are consistent with an “escalator to extinction” process driven by altitudinal compression of climatic niches. Adaptive conservation strategies are urgently needed, including the identification of climate microrefugia and the establishment of connectivity corridors to enhance the long-term persistence of endemic anurans under climate change.

Climate doi: 10.3390/cli14060113

Authors: Richard A. Michelfelder Eugene A. Pilotte Joseph Simmerman

We estimate the production function of CO2 greenhouse gas emissions by the U.S. electric utility industry, responsible for 32% of all CO2 emissions in the U.S. Our results show that the electric utility industry is aggressively mitigating greenhouse gas emissions by substituting generating plant technology and fuel types toward clean energy and exhibits decreasing returns to scale in the production of CO2 emissions. As U.S. electric power generation has been flat, until recently, CO2 emissions have declined.

Climate doi: 10.3390/cli14060112

Authors: Runhe Zheng Fenli Zheng Shouzhang Peng Ximeng Xu Jinxia Fu

Amid the ongoing acceleration of climate change over recent decades, extreme precipitation events have become more frequent and intense on a global scale, triggering severe natural hazards and considerable socioeconomic damage. Nevertheless, how extreme precipitation has evolved at the national level over long time spans, and what role atmosphere–ocean teleconnections play in driving regional differences, remains insufficiently explored. This study addresses that knowledge gap by conducting a comprehensive assessment of eight ETCCDI-based extreme precipitation indices (PRCPTOT, CWD, R20, R95p, R99p, RX1day, RX5day, and SDII) across six climatic sub-regions of China (Northeast, North, East, Central South, Northwest, and Southwest) over 1960–2020, drawing on daily records from 695 quality-controlled meteorological stations. Key atmospheric and oceanic circulation drivers were further diagnosed and their joint influence was quantified via multiple wavelet coherence (MWC). The analysis shows that five of the eight indices (CWD, R95p, R99p, RX1day, and RX5day) underwent statistically significant fluctuating changes (p < 0.05) throughout the 61-year record. Seven indices, all except CWD, demonstrated upward tendencies, with mutation points clustering after 2010, most notably between 2011 and 2016. Wavelet power spectra indicates elevated energy concentrations at multiple time scales, although only CWD exhibited a statistically significant periodicity of approximately 8–10 a (p < 0.05 against red noise). In terms of spatial patterns, index magnitudes generally increased along a northwest-to-southeast gradient. Stations registering significant upward shifts were concentrated in East and Central South China, whereas significant downward shifts appeared mainly in North China and the northern portion of East China. An altitude-dependent pattern was also detected: CWD rose with elevation, while the remaining indices declined sharply below 1288 m, fluctuated in the 1288–2090 m band, and dropped again above 2090 m. Wavelet coherence analysis uncovered significant resonance between extreme precipitation and four circulation indices—SCSMMI, WPSHI, PNA, and NAO. MWC further identified three driver combinations—ENSO-PNA, SCSMMI-WPSHI, and ENSO-NAO-EASMI—as the most influential, acting both individually and synergistically. These results furnish an empirical basis for forecasting, preventing, and managing precipitation-related disasters across China under future climate scenarios.

Climate doi: 10.3390/cli14060111

Authors: Guilherme d. S. Rios Joaquim E. B. Ayer Derielsen B. Santana Victor H. F. d. Silva Marcelo A. R. Pires Talyson d. M. Bolleli Fellipe S. Gomes Mariana Raniero Pedro F. R. Grande Velibor Spalevic Felipe G. Rubira Ronaldo L. Mincato

This study evaluated the spatial and temporal variability of rainfall erosivity (R factor) and its implications for soil loss in the Velhas River Basin, Minas Gerais, Brazil. Rainfall erosivity was estimated from 49 rain gauge stations and CHIRPS precipitation data using empirical equations-based on monthly and annual precipitation totals. Soil loss was estimated using the RUSLE model for the years of minimum and maximum erosivity. Between 2014 and 2024, annual R values ranged from approximately 3900 to more than 9000 MJ mm ha−1 h−1 yr−1, with the lowest values recorded in 2014 and the highest in 2022. Although 2020 had the highest annual rainfall, 2022 showed the highest erosivity, indicating that rainfall intensity and temporal concentration were more important than total rainfall volume. Furthermore, the comparison of erosivity was estimated from ANA stations and derived from CHIRPS agreement for paired station-year observations (r = 0.7196), although CHIRPS slightly underestimated erosivity values (mean bias −5.74%). Estimated soil loss ranged from 0.60 to 274.17 Mg ha−1 yr−1, with the highest values occurring mainly in exposed soil and agricultural areas. These findings highlight the importance of rainfall temporal distribution in erosion risk and support the use of satellite-derived precipitation products for regional-scale erosion assessments in data-scarce tropical basins.

Climate doi: 10.3390/cli14050110

Authors: Elena Corona Elena Belcore Youmanli Enok Ferdinand Combary Fabio Giulio Tonolo Maurizio Tiepolo

In hot, semi-arid zones, cities are experiencing longer and more intense warm spells. Although the literature offers strategies to mitigate this threat, studies verifying their feasibility are limited. In this study, we aim to ascertain the feasibility of reducing land surface temperature (LST) through greening. We combine LST analysis with a feasibility assessment of cooling measures and consider physical and ownership dimensions alongside environmental and social factors, with Ouagadougou (Burkina Faso) serving as a case study. The average LST during the hottest period (April–May) was calculated from ECOSTRESS and Landsat remotely sensed data, and multiple regression models were used to analyse the relationship between LST and land cover/land use across the city’s districts and sectors. Our assessment incorporates greening scenarios, SWOT analyses, and equity assessments, and our results indicate that barren land is the primary determinant of diurnal LST. Planting 0.45 million trees could reduce LST by up to 2.4 °C in peripheral sectors if large roads, utilities, and vacant lands are targeted. This may reduce disparities in tree cover between sectors but could widen the gap between districts. Recommendations include a more hierarchical street network, enhancing utility provision, and reducing barren land in the peripheral sectors.

Climate doi: 10.3390/cli14050109

Authors: Heri Kuswanto Hakan Ahmad Fatahillah Candra R. W. S. W. Utomo Tintrim Dwi Ary Widhianingsih Kartika Fithriasari

Stratospheric Aerosol Injection (SAI) has been investigated as a climate intervention strategy to offset global warming, and regional impacts studies rely on simulations from the Geoengineering Large Ensemble (GLENS). The probabilistic behavior of the GLENS ensemble has not been systematically characterized for Southeast Asia. Because GLENS is a counterfactual experiment combining the Representative Concentration Pathway 8.5 (RCP8.5) forcing with active SAI, comparison with observations cannot validate the SAI response itself. In the early protocol years, the SAI forcing is small, so the early window provides a diagnostic of statistical consistency between the ensemble and the observed climate and of ensemble spread reliability. We compare the 21-member GLENS ensemble for 2020–2025 with ERA5 for daily precipitation and mean and maximum temperature using empirical coverage of the 95% prediction interval, rank histograms with the Jolliffe–Primo decomposition, the Continuous Ranked Probability Score, and the Brier Score for rainfall occurrence. Coverage is well below nominal for all variables, and rank histograms show pronounced U-shapes dominated by the dispersion error component, indicating systematic underdispersion. Because the underlying mechanisms are properties of the ensemble system rather than of the SAI forcing, this underdispersion is expected to persist in the future record, motivating statistical post-processing of GLENS before its use in SAI impact assessments.

Climate doi: 10.3390/cli14050108

Authors: Abiodun Tosin-Orimolade Munshi Khaledur Rahman Oluwaseun Ipede

The Niger Delta region of Nigeria experiences multiple environmental stresses due to intensive oil exploration and pervasive gas flaring, both of which contribute to local and regional climate changes, extreme weather events, and excessive and erratic rainfall. Consequently, flooding remains a recurrent natural disaster, disproportionately impacting the low-lying states of Delta, Bayelsa, and Rivers. This study employs remotely sensed geospatial data and a GIS-based weighted overlay analysis to delineate flood-prone areas on a regional scale in the central Niger Delta states. Flood susceptibility was determined through a weighted overlay of digital elevation model (DEM), slope, proximity to streams, rainfall, and LULC data, among others. Weights of criteria were derived through an analytical hierarchy process (AHP) with a very good consistency ratio of 2.5%. Land use and land cover (LULC) and rainfall data were further analyzed to detect trends of changes between 2012 and 2022. The results show that relatively 77% of the study region is prone to flooding. Areas prone to very high flooding are about 16%, high is 29%, moderate is 32%, while low and very low flood-prone areas cover 18% and 5% of the study region, respectively. There is also a notable increase in average annual rainfall and land cover changes. Average rainfall increased by 58.1% between 2012 and 2017, and by 11.5% between 2017 and 2022. Land cover change analysis further indicates that approximately 1.3% of the study area was converted predominantly to flooded zones and water bodies from 2017 to 2022. The results of this study could be useful for urban regional planning, flood mitigation, and resettlement policies aimed at reducing flood vulnerability and enhancing resilience in the central Niger Delta, as well as other places where similar challenges exist.

Climate doi: 10.3390/cli14050107

Authors: Lokuthula Msimanga Sonwabo Perez Mazinyo Onalenna Gwate

South Africa is highly susceptible to climate variability and long-term climatic shifts, necessitating a comprehensive understanding of changing extreme precipitation patterns to guide effective mitigation and adaptation responses. This study examined variations in extreme precipitation indices from 1981 to 2023 across the eastern Free State Province using daily rainfall records derived from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Ten extreme precipitation indices were evaluated, with trend detection conducted through the Innovative Trend Analysis (ITA) technique. Findings indicate that the majority of municipalities exhibited statistically significant declining trends (p < 0.05) in total wet-day precipitation (PRCPTOT), R99P, R95P, the Simple Daily Intensity Index (SDII), CDD, RX5day, R20mm, and R10mm, suggesting an overall reduction in both heavy and moderate rainfall occurrences. In contrast, significant upward trends (p < 0.05) were identified in CWD, and RX1day, reflecting a shift toward prolonged wet periods and more intense short-duration rainfall events. Taken together, these divergent patterns point to the simultaneous emergence of heightened drought vulnerability driven by reduced cumulative rainfall and increased flood risk linked to intensified precipitation extremes. These results underscore the importance of forward-looking, climate-resilient water resource management and context-specific adaptation strategies suited to the eastern Free State’s complex mountainous terrain.

Climate doi: 10.3390/cli14050106

Authors: Arghavan Omidi Farah Jazuli Gregory D. Hawley Milca Meconnen Dylan Kain Mark Polemidiotis Nam Phuong Do Olamide Egbewumi Andrea K. Boggild

Rising planetary temperatures and extreme heat events have led to an increased incidence of heat-related illnesses, such as heat stroke, globally. Widespread adoption of measures to prevent and treat heat-related illnesses is an increasingly urgent issue given the rising global temperatures; promotion of such evidence-based strategies is needed to reduce heat-related morbidity and mortality globally. Such heat-related environmental illnesses are differentially experienced by those without access to ambient cooling and those engaged in outdoor work and recreation. Moreover, the adverse impacts of heat-related illness experienced by residents of the Global South necessitates the inclusion of high-quality recommendations around prevention and treatment into clinical and public health practice in order to address health equity and human rights considerations. The current guidance on prevention strategies and therapeutic interventions for heat-related illness has been iterated and published by the Wilderness Medical Society (WMS). In this critical appraisal, we have summarized the evidence-based guidelines and highlighted the updated recommendations that reflect evolving issues in heat illness research. Application of the Appraisal of Guidelines for Research and Evaluation (AGREE) II framework has enabled a quality assessment of the guidelines to be performed, which we present herein. The adoption of evidence-based practices around heat-related illness has the potential to reduce morbidity and mortality and improve global population-level health in light of the warming climate.

Climate doi: 10.3390/cli14050105

Authors: Katerina Papagiannaki Vassiliki Kotroni Konstantinos Lagouvardos

Weather-related hazards cause significant societal impacts, yet systematic long-term analyses linking these events to all levels of impact severity remain limited. This study investigates weather-related events and their associated impacts in Greece (2000–2025) using the High-Impact Weather Events Database (HIWE-DB). The HIWE-DB records 626 events, corresponding to 1871 localized records and includes 269 confirmed fatalities. Flood-related hazards are dominant, followed by windstorms, while one-third of all events involve multiple hazardous phenomena. A multilevel analysis, independently assessing weather intensity (W) and impact severity (I), reveals a statistically significant annual increase in the total number of events, driven mainly by low- to moderate-impact events (I1-I2), alongside an increase in high-intensity events (W3). While the most severe events (I3) show high annual variability, they exhibit a 38% increase in the second half of the study period compared to the first. Spatially, societal impacts are predominantly concentrated in major metropolitan areas, whereas the highest per capita fatality rates occur in specific regions, such as West Attica. The findings demonstrate how the independent indicators of intensity and severity contribute to understanding the link between weather hazards and societal exposure, providing an empirical basis for evidence-based risk assessment and impact-based early warnings.

Climate doi: 10.3390/cli14050104

Authors: Jiyong Zhang Hao Chen Rui Mao Xuezhen Zhang

Transmission lines in Southwestern China are highly exposed to compound hazards induced by extreme winds and ice and snow conditions. This study assesses future changes in extreme wind hazards and their spatial overlap with baseline ice susceptibility under the SSP2-4.5 emission scenario, using high-resolution dynamically downscaled climate projections. Compared to the historical period (1995–2014), the results indicate a marked intensification of extreme spring wind events over northwestern Southwestern China and the transitional zone between the Sichuan Basin and the Hengduan Mountains during 2041–2060. The occurrence frequency of wind speeds exceeding historical 50-year return levels is projected to increase by 5–10 times in complex terrain, particularly along the Golmud–Qaidam belt. The Comprehensive Extreme Wind Index (CEWI) identifies the Golmud–Wulanwusu–Qaidam river basin belt as the region of highest wind hazard amplification. Meanwhile, analysis of historical observations reveals that icing-prone conditions occur on more than 25 days each spring in the Nyenchentanglha Mountains and southeastern Tibetan Plateau valleys, establishing a baseline map of ice susceptibility. Due to methodological limitations in projecting future icing, this susceptibility map is used as a static indicator of ice-prone areas. By superimposing projected wind intensification onto the baseline ice susceptibility map, four relative hazard exposure categories are delineated. Regions of highest potential exposure are concentrated in the Bayan Har Mountains and portions of the western Hengduan Mountains, whereas northwestern basins are dominated by high wind risk alone. These results reveal pronounced spatial heterogeneity in the relative amplification of compound hazards under future warming and provide a scenario-informed scientific basis for prioritizing regions in disaster risk reduction and resilient planning of transmission infrastructure in mountainous regions.

Climate doi: 10.3390/cli14050103

Authors: Rizkiana Sidqiyatul Hamdani Sudharto Prawata Hadi Iwan Rudiarto Alfrida Ista Anindya Afrizal Maarif

Slow-onset hazards are intensifying coastal land transformation, yet their socio-environmental implications remain insufficiently understood. The coastal area of Semarang-Demak, Indonesia, represents a critical case due to long-term land subsidence, recurrent tidal flooding, and extensive coastal development interventions. In response to this gap, this study integrates open-access Earth observation with place-attachment perspectives to investigate how urban coastal transformation is materially produced and socially experienced. Multi-temporal Landsat imagery from 1994 to 2024 was processed in Google Earth Engine using the Modified Normalized Difference Water Index (MNDWI), complemented by the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI). The results show spatially uneven coastal land transformation, with 13.02 km2 of the study area indicating increased MNDWI values (to-water transformation), while 11.75 km2 experienced to-land transformation associated with declining MNDWI values. Further analysis using NDVI and NDBI suggests that part of the to-land transformation reflects anthropogenic built-area expansion, as indicated by areas where NDBI differences exceed NDVI differences. Empirical field observations and interview data contextualize these spatial findings by revealing contrasting yet persistent place attachment across reclamation-influenced areas and communities exposed to erosion and flooding. Building on these findings, the study proposes the notion of the (un)disrupted place to explain how disruption, efforts for resilience and continuity coexist unevenly across coastal space. This study advances a socio-environmental understanding of coastal land transformation and highlights the need for more equitable and multidisciplinary approaches to coastal governance and resilience planning.

Climate doi: 10.3390/cli14050102

Authors: Harry D. Kambezidis

This study investigates the influence of large-scale climate modes on Mediterranean dust-transport variability using a newly developed Saharan Dust Flux Transport Index (SDFTIbase) for 2003–2024. Monthly and seasonal correlations show that NAO–SDFTIbase associations reach r = 0.35–0.55 across sub-regions, whereas ENSO–SDFTIbase correlations remain weaker (r = 0.10–0.25). Running correlations reveal pronounced non-stationarity, fluctuating between −0.4 and +0.6, while wavelet coherence exceeds 0.5 at 2–4-year periods during episodic teleconnection events. NAO exerts its strongest influence at sub-annual scales (0.15–0.5 years), whereas ENSO modulates dust transport primarily at interannual scales (1–3 years). Teleconnection strength is regionally heterogeneous: WestMed and EastMed exhibit the most persistent coupling, CentMed shows weak sensitivity, and BalBSea displays intermediate behaviour. NAO produces near-immediate dust-transport responses, while ENSO often leads dust-transport variability. These results provide a multi-scale dynamical framework linking Atlantic and Indo-Pacific climate variability to Mediterranean dust-transport pathways and highlight the importance of teleconnection-based diagnostics for regional climate assessment.

Climate doi: 10.3390/cli14050101

Authors: Jiani Li Yanjun Qi Zhihua Zhang Shuangyan Yang Yu Ouyang

Based on gridded rainfall data and reanalysis datasets during the period 1979–2021, this study investigates the phase-dependent modulation of ENSO (El Niño–Southern Oscillation) on the interannual relationship between summer rainfall and intraseasonal oscillation (ISO) intensity over the middle-lower reaches of the Yangtze River Basin (YRB), together with the associated physical mechanisms. The results show that summer rainfall over the YRB exhibits prominent intraseasonal variability and is significantly positively correlated with ISO intensity at the interannual timescale. This interannual correlation is strongly dependent on the phase of ENSO. During the developing phase of El Niño summers, both summer rainfall and ISO intensity over the YRB are significantly suppressed, and their interannual relationship becomes statistically insignificant. In contrast, during the decaying phase of El Niño summers, both rainfall and ISO intensity are remarkably enhanced, with their positive interannual correlation being substantially strengthened compared to the climatological mean. Further analysis indicates that ENSO influences YRB summer rainfall and ISO intensity primarily by modulating the structure and amplitude of the East Asia–Pacific (EAP) teleconnection pattern. These EAP-related circulation anomalies alter the large-scale atmospheric circulation and moisture transport conditions over the YRB, leading to adjustments in both summer mean rainfall and its intraseasonal variability. Such adjustments not only modify the magnitudes of rainfall and ISO anomalies but also reshape their interannual covariability, resulting in the distinct characteristics of their relationship observed between the developing and decaying phases of El Niño. Therefore, ENSO acts as a key regulator of summer rainfall, ISO intensity, and their interannual relationship in the YRB through its phase-dependent modulation effects.

Climate doi: 10.3390/cli14050100

Authors: Amr Mohamed Abdelkhalek Ayman Georges Awadallah Nabil Ahmed Awadallah

The impact of climate change on rainfall extremes has become increasingly obvious in many climatic regions including arid regions where extreme precipitation events are thought to have augmented or at least intensified. Driven by global factors such as greenhouse gas emissions, deforestation, and industrialization, climate change has augmented hydrological variability, thus making traditional stationary models inadequate for the estimation of extreme rainfall at various return periods. Extreme value analyses, which were traditionally derived under the assumption of stationarity (i.e., constant statistical properties over time) and typically do not account for temporal variability or external climatic drivers (e.g., temperature or large-scale climate indices), may lead to inaccurate estimation of rainfall quantiles under changing climate conditions. This paper presents a structured review of applied methodologies for quantifying the influence of climate change on extreme rainfall events, with special attention to how non-stationarity is addressed in arid regions applications, which was not a major focus in previous review papers. Relevant statistical techniques, extreme value theory, machine learning models, and high-resolution climate simulations are reviewed. From an initial pool of over 340 studies, 91 were selected based on their relevance to quantify rainfall extremes induced by climate change in arid regions. Based on the reviewed studies, the analysis revealed a strong reliance on trend analysis of downscaled Global Climate Models (GCMs) and Regional Climate Models (RCMs) within a stationary framework, with limited integration of covariates, other than time, in non-stationary frequency analysis to estimate the climate change-related value. This review identifies the research gaps in the scientific literature related to climate change impact assessment on extreme rainfall in arid regions. It emphasizes the necessity for adopting more robust hybrid approaches, adopting statistical distributions more suitable to arid conditions, careful treatment of outliers, conducting regional analyses to better understand the overall climate behavior of the region, addressing the impact on short-duration rainfall, integrating key climatic drivers through the incorporation of additional climate covariates and the impact of climate change on sub-daily rainfall patterns.

Climate doi: 10.3390/cli14050099

Authors: Dan Seidov Alexey Mishonov James Reagan

The circulation of the North Atlantic Ocean plays a vital role in the Earth’s climate system. Numerous studies, primarily through computer simulations, have examined the stability of the Atlantic Meridional Overturning Circulation (AMOC) in a warming climate. Some of these studies predict a potential collapse of the AMOC in the foreseeable future, which would require a significant influx of freshwater into the subpolar North Atlantic (NA) and Nordic Seas. Paleoreconstructions of NA circulation indicate a major shift in the position of the subpolar cold front, which either precedes or coincides with substantial changes in AMOC dynamics. These changes in the front position imply a significant alteration in circulation patterns, beginning with the noticeable restructuring of the subtropical and subpolar gyres. This would lead to modifications in the Gulf Stream system and the North Atlantic Current (NAC), affecting the thermohaline fields and the position and strength of these two current systems. Although some models predict a significant slowdown or even collapse of the AMOC, recent observational studies have offered a more cautious perspective. For instance, the Gulf Stream system exhibits high resilience to perturbations caused by ongoing sea surface warming. In this study, we analyzed the decadal variability of temperature and salinity from in situ observations, along with upper-ocean currents in the subpolar NA (SPNA). We found that the thermohaline pattern of the upper ocean layers in the SPNA and Nordic Seas has remained resilient for over 70 years. The deceleration of the AMOC is evident but relatively modest, with average velocities in the upper layers decreasing by less than 10–15% over 30 years. This deceleration was also inconsistent throughout the NAC region. Furthermore, the subpolar front migration over 70 years, as manifested in isotherm spatial variability, reached a maximum of 3° of latitude, with spatial variability of the yearly 10 °C isotherms being lower. Overall, the conclusion regarding the resilience of the NAC aligns well with that of the Gulf Stream, with no substantial changes in the position or intensity of the subpolar gyre. We conclude that while the AMOC is susceptible to some deceleration due to ongoing surface warming and/or high-latitude freshening, it may also be sufficiently resilient to withstand these changes. Although it cannot be entirely ruled out that the AMOC may reach its tipping point within this century, an analysis of data on decadal variability in the upper arm of the AMOC suggests that such a collapse is unlikely to occur.

Climate doi: 10.3390/cli14050098

Authors: Kécia M. R. Silva Helber B. Gomes Robson B. dos Passos Ismael G. F. de Freitas Fabrício D. dos S. Silva Maria C. L. da Silva Dirceu L. Herdies Henrique M. J. Barbosa

Reanalysis datasets provide gridded, high-frequency estimates of atmospheric variables that are essential for studying weather and climate, particularly in regions with sparse observational networks. Despite their widespread use, the quality of reanalysis products remains insufficiently validated in tropical regions, particularly in areas with complex terrain. In this study, we evaluate the performance of surface-level temperature and atmospheric pressure fields from ERA5 and ERA5-Land in the state of Alagoas, northeastern Brazil. The analysis is based on a 12-year comparison (2008–2019) with observational data from the National Institute of Meteorology (INMET). Prior to validation, altitude corrections were applied to minimize elevation-induced biases in the reanalysis fields. Performance was assessed using statistical metrics. Both reanalyses showed strong agreement with observations, with average correlations exceeding 0.91 for temperature and pressure. ERA5 temperature biases ranged from −0.2 °C to 0.3 °C, and those for ERA5-Land from −0.6 °C to −0.3 °C, with RMSE around 1.6 °C. Pressure biases were initially larger (−20 hPa to +6 hPa in ERA5), but were reduced to below 0.5 hPa at key reference stations after correction. Diurnal and seasonal cycle analyses confirmed the datasets’ ability to reproduce temporal variability, though both reanalyses tended to overestimate minimum temperatures and underestimate maximum temperatures. Further investigation is needed to identify the origin of anomalous temperature jumps in ERA5’s diurnal cycle, which seem unrelated to the assimilation cycles. Overall, the results highlight the robust performance of ERA5 and ERA5-Land in representing surface atmospheric conditions in tropical coastal regions, while also emphasizing the continued need for regional validation and preprocessing before application in high-resolution or short-term studies.

Climate doi: 10.3390/cli14050097

Authors: Joairia Hossain Faria Sabina Yeasmin Sanjana Hossain Nijhum A. K. M. Mominul Islam Md. Parvez Anwar

Globally, greenhouse gas (GHG) emissions have risen dramatically due to accelerated industrialization, excessive fossil fuel extraction, and agricultural activities, leading to global warming and ecosystem collapse. Achieving net-zero carbon emissions has therefore become a crucial global priority. Despite substantial international efforts, only a small number of countries have achieved carbon neutrality so far, with the majority aiming to do so by 2050 or 2060. Progress remains hindered by fragmented international coordination and inadequate integration of mitigation and adaptation co-benefits. However, agriculture is a major carbon emitter with significant mitigation potential. Attaining local carbon neutrality in agricultural landscapes is highly costly and strongly impacted by the spatial heterogeneity of GHG emissions and the diversity of available mitigation possibilities. This sector remains a major contributor to methane (CH4) and nitrous oxide (N2O) emissions, mainly through enteric fermentation and fertilizer use, and thus must be prioritized in global carbon neutrality strategies. Tactics such as improved livestock management, reduced use of synthetic fertilizers, conservation agriculture, afforestation, and renewable energy adoption can reduce emissions. These technical approaches should be supported by effective policy instruments, like carbon taxes, cap-and-trade schemes, low-carbon practice subsidies, and regulatory frameworks. Together, these measures can enable a transition toward long-term sustainability in agriculture by balancing emissions with removals through enhanced carbon sinks and credible offset mechanisms.

Climate doi: 10.3390/cli14050096

Authors: Jitendra Bikram Shahi Bed Mani Dahal Nani Raut Sunil Kumar Pariyar Nabin Aryal

The agriculture sector in Nepal is highly vulnerable to climate change due to its traditional practices, limited technological intervention, and low adaptive capacity. Owing to the country’s complex topography, the impacts of climate change are spatially heterogeneous, making local-level climate change assessments highly relevant. This study focuses on the impact of climate change on three major crops (rice, wheat, and maize), in the Doti district of Nepal, based on meteorological records, crop yield data, questionnaire surveys, and focus group discussions. Climate records from 1982 to 2022 show a trend in annual rainfall at a rate of −3.28 mm per year, with a particularly pronounced decline during the monsoon season. Both maximum and minimum temperatures exhibit statistically significant increasing trends of 0.01 °C and 0.03 °C per year, respectively. The most significant warming for maximum temperature occurs during the monsoon season, while minimum temperature shows the highest increase during the pre-monsoon season. During the same period, annual yields of paddy, maize, and wheat show statistically significant increasing trends. These trends in climate variables and crop yields align with the perceptions of local communities. Linear correlation analysis indicates that maximum and minimum temperatures have a positive influence on crop yields, whereas precipitation and diurnal temperature range have negative effects. Among these, minimum temperature has the greatest impact on crop yields, followed by maximum temperature and rainfall. Multiple linear regression analysis reveals that climate variables better explain long-term trends in crop yields rather than year-to-year variability. The impact of climate is most pronounced in wheat where climate variables account for approximately 55% of the yield variability, followed by paddy (R2~49%) and maize (R2~20%). Despite the overall increase in crop yields, interannual variability has grown, consistent with increased variability in climate parameters. To cope with this uncertainty, local communities have adopted various adaptation strategies, including the use of improved seed varieties, green manure, and changes in crop types. Other key practices include the use of inorganic fertilizers, selection of short-duration crops, crop rotation, minimum tillage farming, and river conservation.

Climate doi: 10.3390/cli14050095

Authors: Karim Malik Isteyak Isteyak Kristen Kys Yusriyah Rahman Hala Al Daker Karanveer Sidhu

Snow water equivalent (SWE), the amount of water that will be liberated when a given snowpack melts, is considered an essential climate variable. Snowmelt drives annual run-off in snow-dominant basins. However, detecting daily SWE changes in lake-effect snowfall regions such as the Great Lakes Basin (GLB) is challenging with classical methods. We developed a Siamese U-Net (Si-UNet) model to detect and characterize daily changes and trends in SWE. Our Si-UNet detected daily changes in SWE over the GLB with an F1-score of 98.73%. To characterize the basin-wide extent of anomalies in SWE distribution, we compared SWE trends to a 35-year median (35YB) baseline and identified decadal trends in SWE. We found that the period from 1989 to 2008 was the temporal window with minimal anomalies, compared to the 35YB of ~0.5108. Positive deviations from the 35YB were prevalent over these 20 years, indicating less significant daily changes. A significant shift to daily SWE similarity below the 35YB occurred after 2009, especially in January and February. Daily changes in SWE were high in April, beginning in the second week. The strongest positive trend, likely associated with lake-effect snowfall, was observed in April 2000 (R2 = 0.47).

Climate doi: 10.3390/cli14050094

Authors: Sayed Alim Samim Abdul Qadir Nabizada Miraqa Hussain Khail Zhiquan Hu Sebastian Stepien

Afghanistan is highly vulnerable to the effects of climate change, which poses significant challenges to food security and environmental systems. To mitigate these challenges and promote sustainable development, it is important to adopt an integrated method that promotes food production and climate resilience for environmental sustainability. This manuscript aims to estimate the decoupling impact of green energy on CO2 emissions and food crop production in Afghanistan, with a focus on promoting Sustainable food production. In this research article, the Nonlinear Auto Regressive Distributed Lag (NARDL) model was used to estimate data from 1996 to 2021 in Afghanistan. The NARDL bounds test confirms a stable long-run equilibrium relationship between climatic factors and food crop production. The long-run results reveal an asymmetric decoupling impact of green energy on CO2 emission and food crop production. Specifically, a 1% positive or negative shock in the interaction between green energy and CO2 emissions produces different outcomes for food crop production. Increasing temperature tends to decrease food production, while precipitation increases food production over the long term. Furthermore, raising CO2 emissions negatively affects long-term food production, while greater use of green energy contributes to food production in the future. These findings underscore the need to adopt climate-resilient technologies, including climate-smart agriculture, to help farmers withstand the adverse effects of climate change. In addition, to ensure long-term stability in food production, Afghanistan should prioritize the development of green technologies. This approach would reduce agriculture’s dependence on fossil fuels and foster the growth of sustainable agricultural industries.

Climate doi: 10.3390/cli14050093

Authors: Muhammad Attiq Khan Ulrich Foelsche

Mediterranean low-pressure systems or cyclones are responsible for many extreme events affecting the region. This study presents a comprehensive analysis of Mediterranean cyclones from 1940 to 2024 using high-resolution ERA5 reanalysis data. This study implements a detection algorithm based on geopotential height minima on three different pressure levels (1000 hPa, 850 hPa and 700 hPa). Cyclone tracks in this study are constructed by linking identified low-pressure centers at successive time steps using a nearest neighbor tracking algorithm. The number of cyclones at 1000 hPa is filtered by matching them with upper levels and restricting them within 150 km from the coast, covering the entire Mediterranean region, which we divided into three subregions: the western Mediterranean, the eastern Mediterranean, and the Black Sea. Seasonal analysis was performed for winter (December–February), spring (March–May), summer (June–August), and autumn (September–November). Our results have recorded 39,933 individual cyclone tracks, where the majority (25,265 cyclones; 63.3%) are short-lived (24–72 h). Regionally, the western Mediterranean has the highest cyclone density, followed by the Black Sea and the eastern Mediterranean. While there is only a small increase in total numbers, a notable increase in cyclone activity is observed during the summer months, particularly in August, with a statistically significant rise of 18.4% since 1980 across the whole Mediterranean region. In the western Mediterranean, this August intensification was even 23.8%. As a result of this, the annual peak of cyclone activity has shifted from May/June to August.

Climate doi: 10.3390/cli14050092

Authors: Crizzia Mielle De Castro Paul E. Roundy

Atmospheric equatorial Kelvin waves—convective disturbances that manipulate tropical wind and rainfall patterns—can propagate eastward at speeds ranging from nearly stationary to 30 m/s, with variability determined by moist processes and advection by the background wind. Current studies on Kelvin waves lack a comprehensive climatology that explains how their structure and propagation speeds change in different background states. Thus, this work builds a variable regression model that uses ERA5 reanalysis data to reconstruct Kelvin waves during different background wind shear conditions and phases of the Madden–Julian Oscillation (MJO) and the El Niño–Southern Oscillation (ENSO) over the Pacific. Overall, Kelvin waves tend to speed up during background conditions that generate upper-tropospheric westerlies and slow down during upper-tropospheric easterlies. East Pacific Kelvin waves are faster than West Pacific Kelvin waves because of climatological westerly shear in the former and easterly shear in the latter. However, strong westerly shear over the East Pacific allows extratropical Rossby waves to impede on the Kelvin wave, while strong easterly shear over the West Pacific distorts classical Kelvin wave structure. The results provide references for weather prediction models to accurately resolve the interaction between Kelvin waves and background circulation.

Climate doi: 10.3390/cli14050091

Authors: Patrick Haertel David Torres

Tropical cyclones (TCs) and the Madden–Julian Oscillation (MJO) are two of the most impactful weather systems in the tropics. For example, it is not uncommon for a strong TC to kill hundreds of people and cause tens of billions of dollars in damage. The MJO modulates not only TCs but also monsoons around the world, which contribute essential rainfall for agriculture that supports billions of people, but which also can cause deadly floods. Because of the close coupling between the MJO and TCs, as well as the several week predictability of the MJO, models that can accurately simulate both kinds of weather systems have the potential to be useful for both mid-range weather forecasting and studies of impacts of climate change. This paper describes the further development of one such model, the Lagrangian Atmospheric Model (LAM), which simulates atmospheric motions by predicting motions of individual air parcels, and which has been shown to accurately simulate the MJO in previous studies. In this study, a new parameterization of cloud albedo is included in the LAM, and the model is tuned to improve simulations of TC distributions while still maintaining a robust and realistic MJO. Objective metrics of the model basic state, MJO quality, and TC distributions are used to optimize parameter selections for the cloud albedo parameterization and convective mixing. After tuning the LAM using dozens of 3-year simulations, we conduct two longer simulations forced with observed sea surface temperatures to verify that the new version of LAM has a substantially improved representation of TCs while still maintaining a realistic MJO.

Climate doi: 10.3390/cli14050090

Authors: Andrea Abbate Leonardo Mancusi Michele de Nigris

The power system is the backbone of the energy network, and overhead lines are its vital structures. Weather threats may jeopardise the reliability of lines and make them a weak link. In particular, heavy rainfall episodes can cause failures, especially in mountain areas. Current climate changes may exacerbate the effects on the ground, intensifying rainfall episodes and increasing the frequency of extreme events. In this context, debris flows triggered by rather intense precipitation and characterised by fast kinematics can destroy pylons and electric connections, affecting the infrastructures not only in the upper ridges but also downstream across the fan apex, where powerlines are much more distributed. This study presents an in-depth back-analysis of two debris flow events triggered in concomitance with a heavy cloudburst that occurred in Talamona (Sondrio Province, Italy) in July 2008 and in Campo Tartano (Sondrio Province, Italy) in April 2024. These events hit onsite powerlines, causing blackouts and showing the potential vulnerabilities of the local electricity system. An analysis of rainfall-induced landslide failure is carried out using the numerical model CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment) and MIST-DF (Modelling Impulsive Sediment Transport—Debris Flow) with the aim of reconstructing the dynamics of the first (i.e., Talamona) geo-hydrological event. Powerline vulnerability is also investigated against debris flow dynamics, discussing possible strategies to reduce pylon exposure and to increase the resilience of the local electro-energetic network. Since, under climate change scenarios, heavy rainfall episodes are projected to intensify, an alternative approach based on rainfall-threshold curves is presented and applied to both cases of study. The latter, already implemented for civil protection purposes, could be useful in early-warning procedures against potential debris flow hazards. For both methodologies, the findings from the study confirm the strength of the approaches and foster their application in different situations (back-analysis and early warning) to reduce powerlines’ geo-hydrological risks.

Climate doi: 10.3390/cli14050089

Authors: Hanns Moshammer Martin Jury Alexandra Kristian Lisbeth Weitensfelder Hans-Peter Hutter

Climate change will impact the distribution of daily deaths in Austria until the end of the century. This study examines the net effects of fewer cold and more-frequent hot days on daily mortality under different climate and demographic scenarios. Projected district-level mortality data and daily temperatures based on Representative Concentration Pathways (RCP4.5 and RCP8.5) are analyzed to estimate the number of attributable deaths for every fifth year due to heat and cold using district-wise temperature–effect estimates from a previous analysis. While the overall shape of the time course of temperature-attributable deaths depends mostly on the demographic developments (with the highest numbers of daily mortality mid-century), under all climate scenarios investigated, the increase in heat-attributable deaths will be more pronounced than the decrease in cold-attributable deaths. Contrary to common claims, shift in temperatures due to climate change already has a net negative effect on population health in Austria now.

Climate doi: 10.3390/cli14040088

Authors: Daniel Berhanu Tena Alamirew Greg O’Donnell Claire L. Walsh Amare Haileslassie Temesgen Gashaw Tarkegn Amare Bantider Solomon Gebrehiwot Gete Zeleke

East Africa is highly vulnerable to climate change due to limited adaptive capacity and strong reliance on rain-fed agriculture. Ethiopia, in particular, experiences recurrent socio-economic losses from droughts and floods. This study presents a national-scale assessment of observed (1981–2010) and projected (2041–2100) changes in extreme seasonal precipitation across Ethiopia using ten ETCCDIs. High-resolution Enhancing National Climate Services (ENACTS) observations and bias-corrected outputs from a selected ensemble of CMIP6 models under SSP2-4.5 and SSP5-8.5 scenarios are used to assess historically trends and future extreme precipitation, respectively. Historical trends show increases in extreme precipitation during the Kiremt (JJAS) season, particularly over the northwestern, western, and southwestern highlands; however, most of these increases are not statistically significant. In contrast, the Belg (FMAM) season exhibits widespread declines, which are also largely not statistically significant. Future projections suggest increases in total precipitation (PRCPTOT), heavy (R10) and very heavy rainfall days (R20), very wet days (R95p) and extremely wet days (R95p), and rainfall intensity (SDII) over northwestern, western, southwestern, and parts of northeastern Ethiopia during JJAS. During FMAM, PRCPTOT is projected to increase in the northern and northwestern regions, while decreases are expected in the northeastern and southeastern regions. The Awash and Tekeze basins emerge as key hotspots of change, indicating potential seasonal shifts and an increased likelihood of extreme weather in these regions. Despite inter-model uncertainty, the results highlight the need for flexible, uncertainty-informed adaptation strategies to enhance climate resilience in Ethiopia.

Climate doi: 10.3390/cli14040087

Authors: João Lucas Della-Silva Fernando Saragosa Rossi Damien Arvor Gabriela Souza de Oliveira Larissa Pereira Ribeiro Teodoro Paulo Eduardo Teodoro Tatiane Deoti Pelissari Wendel Bueno Morinigo Carlos Antonio da Silva Junior

The northern part of the state of Mato Grosso is located at the intersection of large-scale agricultural production and the Amazon, a tropical biome of great importance for ecosystem services and biodiversity. Agricultural production activities interact with natural capital, among other factors, in land use and in biogeochemical cycles of water and carbon. In this study, we sought to use remote sensing at the regional level to diagnose and spatialize the contribution of agricultural activity to dry areas. Using carbon dioxide orbital models, land use classification techniques, the Standardized Precipitation Index (SPI), and Pettitt and Mann–Kendall statistics, the variables were compared spatially for the biogeographic boundary of the Amazon in Mato Grosso in two distinct time frames: (i) over the crop years of the CO2 efflux model (2020 to 2023), and (ii) over the years 2008 to 2023, with consolidated data from the MODIS sensor system. The hot and cold spots analysis reinforces the correlation of carbon variables to land use; the drought index suggests a spatial correlation to forest loss, where more intense agricultural activity favors drought and inhibits moderate rainfall, and in turn is linked to the amount of forest in the context of intense continentality. Temporally, the statistical diagnosis highlights abrupt changes in 2011, 2013, and 2019, restate the complex relation of tropical forest and biogeochemical cycles, above all with carbon dioxide.

Climate doi: 10.3390/cli14040086

Authors: Mohammed Mussa Abdulahi Pascal E. Egli Anteneh Belayneh Yazidhi Bamutaze Charlotte Anne Nakakaawa Sintayehu W. Dejene

Nature-based solutions (NbSs) are increasingly recognized as sustainable and cost-effective strategies for mitigating drought impacts. However, robust quantitative evidence on the effectiveness of NbSs for drought mitigation, especially under future climate change scenarios, remains limited. In particular, the extent to which grazing management can reduce agricultural and hydrological droughts over long time horizons is still poorly understood. This study examines the long-term effectiveness of grazing management as a NbS for mitigating drought under historical and future climate conditions in the Ganale Dawa River Basin, Ethiopia. We combined remote sensing, machine learning, and climate projections to simulate soil moisture and runoff using a long short-term memory (LSTM) model. Protected areas were used as proxies for light grazing, while adjacent non-protected areas represented heavy grazing. Agricultural and hydrological droughts were quantified using the standardized soil moisture index (SSMI) and standardized runoff index (SRI), respectively. The results show that light grazing consistently reduced drought severity compared to heavy grazing across all periods. Agricultural drought severity was reduced by up to ~15% under SSP2-4.5 and SSP5-8.5, while hydrological drought severity showed substantially larger reductions, exceeding ~40% in mid- and late-future periods. Differences between grazing regimes widened under stronger climate forcing, indicating that grazing management benefits become more pronounced under future climate stress. These findings demonstrate that grazing management is an effective NbS for enhancing long-term drought resilience. Scaling up sustainable grazing practices could, therefore, serve as a practical climate adaptation strategy for drought-prone basins in Ethiopia and similar regions.

Climate doi: 10.3390/cli14040085

Authors: Corrie Monteverde Fernando De Sales Trent W. Biggs Katrina Mullan Charles Jones Mariana Vedoveto

Deforestation in unprotected areas (UPAs) within the Brazilian Amazon affects environmental sustainability and regional climate. This study quantifies shifts in near-surface air temperature, precipitation, and evapotranspiration (ET) during the dry season resulting from UPA loss. Utilizing a five-year ensemble (2015–2019) to isolate the climatic response from interannual variability, simulations indicate a warmer (+1.0 ± 0.4 °C) and drier climate, characterized by a basin-wide 12 ± 8% reduction in precipitation and a 12 ± 4% reduction in ET following UPA removal. This shifted climate state extends to Rondônia, a southwestern state where detailed risk mapping was developed by integrating changes in climate variables with socio-economic, agricultural, and demographic. UPA deforestation, largely external to Rondônia, is associated with a simulated decrease in precipitation by 20 ± 7% and ET by 11 ± 9% coupled with an increase in air temperature by 1.2 ± 0.4 °C. These shifts indicate increased vulnerability for municipalities, including the capital, potentially affecting agricultural productivity. Findings suggest that to protect remaining forests these biophysical risks must be mitigated. This study establishes a spatial framework for identifying municipalities most suceptible to the climatic shifts triggered by UPA loss.