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The Loess Plateau in China is highly susceptible to frequent landslides and other geological disasters, which have led to substantial losses of natural and human resources and are frequently reported in the news media. Yuzhong County, located east of Lanzhou City, is a mountainous region with considerable development potential. On 7 August 2025, this area experienced a large-scale geological disaster characterized by a compound event involving both landslides and debris flows, resulting in nearly several hundred casualties. With the ongoing urban expansion of Yuzhong County in recent years, the prediction and prevention of geological disasters have become increasingly critical. This study employed three machine learning algorithms—Multiple Logistic Regression (LR), Random Forest (RF), and XGBoost (XG)—to assess landslide susceptibility in Yuzhong County. A total of 169 historical landslide points, supplemented by additional sites identified through field investigations, were compiled, along with 200 non-landslide locations. Multiple environmental factors were incorporated into the models to analyze landslide susceptibility across different areas. Because LR can effectively capture the generalized influence of precipitation variability, it was selected as the primary model for the final susceptibility mapping. To more accurately evaluate the impact of precipitation on landslide occurrence, average seasonal precipitation across the four seasons was used as a predictive factor. To refine the risk assessment at the township level, both raster-based and landslide-unit-based evaluation approaches were adopted. Overlay analyses were then performed by integrating urban infrastructure, population distribution, and predicted landslide hazard zones, while also accounting for the potential influence of extreme precipitation events. The results reveal that the mountainous areas in eastern Mapo Township, southern Xiaokangying Township, southern Xiaguanying Town, and the south-central part of Qingshuiyi Township are high-risk zones prone to group-occurrence landslide disasters. Full article

The dynamics of aboveground biomass (AGB) in rubber plantations (RPs) provide an important basis for evaluating carbon stocks and environmental adaptability in tropical plantations. However, continuous monitoring of AGB of RPs at the regional scale is lacking, and its nonlinear responses to hydrothermal conditions remain insufficiently understood. This study focused on RPs in western Hainan Island (WHI), including Danzhou, Baisha, Lingao, and Chengmai, and integrated field plot data with multi-source remote sensing datasets. A framework for mapping RPs combining rule-based constraints and phenology-based random forest (RF) classification was developed. After key variable screening, extreme gradient boosting (XGBoost), Shapley additive explanations (SHAP), and generalized additive model (GAM) were used for AGB estimation and identification of climatic responses. The results showed that mapping of RPs achieved an overall accuracy of 92.89% and a Kappa coefficient of 0.854. The XGBoost-derived estimates showed that AGB of RPs in the study area increased by approximately 1.43 × 10⁶ Mg from 2017 to 2025, with growth areas mainly concentrated in the Danzhou–Baisha and western Chengmai. AGB exhibited significant nonlinear responses to climatic factors. Specifically, the effect of precipitation (PRE) shifted to negative after approximately 1945 mm yr⁻¹, whereas annual mean maximum temperature (TMAX) shifted to a positive effect after about 29.72 °C, although this effect gradually weakened as temperature continued to rise. Combinations such as PRE × annual mean temperature (PRE × TMP), PRE × TMAX, and PRE × potential evapotranspiration (PRE × PET) exhibited significant nonlinear interactions, indicating that the direction and magnitude of the effect of PRE shifted with changes in temperature and PET levels. These findings link the spatiotemporal changes in AGB of RPs in WHI with hydrothermal thresholds and their interacting effects, deepening our understanding of the climatic response characteristics of AGB in RPs in this region. They also provide a scientific basis for RP monitoring, carbon stock assessment, and climate-adaptive management in WHI. Full article

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. Full article

Accurate precipitation data are critical for hydrological and climatic studies in Guangdong Province, where complex terrain and frequent extreme rainfall pose substantial challenges. However, the performance of gridded precipitation products is still not well understood. This study evaluates nine products, including gauge-based (CHM_PRE, CN05.1, GMCP, NOAA CPC), satellite-based (IMERG-E, IMERG-F, TMPA RT, TMPA 3B42), and ERA5 reanalysis against NCDC observations from 2001 to 2019 using metrics including trend significance, correlation (R), root mean square error (RMSE), categorical statistics (POD, FAR, ETS), and relative bias across rainfall intensities. The results indicate that, based on validation against NCDC observations, CHM_PRE performs the best across all temporal scales, capturing significant increasing trends (p < 0.05) and achieving the highest consistency with observations at the annual (R = 0.99), monthly (R = 0.99), and daily (R = 0.89) scales. Using CHM_PRE as the reference, CN05.1 shows the highest spatial consistency with it, especially for extreme events. NOAA CPC exhibits the best performance in monthly event detection (ETS = 0.42; BIAS ≈ 1). Satellite products show acceptable performance at the monthly scale but exhibit intensity-dependent biases and high daily variability, with pronounced “light rain overestimation and heavy rain underestimation.” ERA5 shows limitations, particularly in its severe underestimation of extreme precipitation. CHM_PRE is thus identified as the most suitable dataset for Guangdong based on its agreement with NCDC observations. With CHM_PRE as the reference, CN05.1 provides a reliable alternative for spatial analyses; NOAA CPC performs the best in monthly event detection. Satellite products suit monthly use but require daily-scale caution; ERA5 shows a relatively poor performance. Full article

Araucaria angustifolia (Bertol.) Kuntze, a critically endangered tree species, plays an irreplaceable ecological role in its native habitats. Under global climate change, identifying the drivers governing its geographic distribution and assessing climate-related threats can provide scientific guidance for the long-term conservation and habitat restoration of this species. In this study, a total of 287 valid occurrence records from 27 countries were compiled. Combined with 14 screened environmental variables, an optimized Maximum Entropy (MaxEnt) model was used to predict the potential suitable habitats of A. angustifolia under historical climate conditions (1970–2000), as well as under low-emission (SSP126) and high-emission (SSP585) scenarios for the future periods of 2050, 2070, and 2090. Under historical climatic conditions, the average training AUC value from 10 replicate model runs was 0.979, indicating excellent and reliable model performance. Globally, the species has 1.91 × 10⁶ km² of moderately suitable habitat and 0.95 × 10⁶ km² of highly suitable habitat, with a total suitable habitat area of 2.86 × 10⁶ km², accounting for only 1.92% of the global terrestrial area. Mean annual temperature (bio1), mean temperature of the coldest quarter (bio11), and annual temperature range (bio7) are the dominant environmental variables shaping the distribution of A. angustifolia, followed by annual precipitation (bio12). Under future climate scenarios, the overall suitable habitats of A. angustifolia exhibit a slight contracting trend, whereas their spatial distribution patterns remain relatively stable. Full article

Xylem sap is increasingly recognized as a dynamic biological matrix reflecting whole-plant physiological status, but its biochemical variation under field conditions remains insufficiently characterized. We investigated oxidative stress markers, osmolytes, antioxidant enzymes, and redox-related enzymes in xylem sap from three focal tree individuals representing Fraxinus excelsior, Populus nigra, and Pinus sylvestris. Sap was collected by passive stem tapping using a custom-built apparatus, and biochemical patterns were evaluated using multivariate statistical and machine-learning approaches. The three focal trees showed distinct biochemical profiles within the present dataset. The focal P. nigra individual was associated with relatively higher antioxidant enzyme activities, whereas the focal F. excelsior and P. sylvestris individuals were more closely associated with oxidative-damage and metabolic-adjustment traits. Precipitation and wind direction were retained as the main meteorological variables associated with biochemical variation, with wind direction interpreted as an atmospheric correlate rather than a direct physiological driver. Exploratory machine-learning analyses highlighted catalase and selected meteorological variables as influential predictors. Overall, the findings support the potential of xylem sap for integrative ecophysiological monitoring while emphasizing the exploratory nature of patterns derived from repeated measurements of three focal trees. Full article

Atmospheric water vapor is a major driver of Earth’s climate, yet despite its vital role in driving extreme weather and informing Numerical Weather Prediction (NWP) models, precise quantification of Precipitable Water Vapor (PWV) remains a challenge due to its high spatial and temporal variability. To provide an upgraded evaluation reflecting the most recent data and next-generation instrumentation, this study evaluates the accuracy, relative to radiosonde measurements, in calculating PWV values across six different instruments: Microwave Radiometer (MWR), NOAA-21, TROPOMI, Pandora spectrometer, AERONET sun-photometer, and GNSS/GPS against the bias-corrected Vaisala RS-92 and RS-41 radiosondes over Beltsville, Maryland, utilizing an updated 2024–2025 database. As a certified GRUAN site, HUBC adheres to strict international observation standards designed specifically to provide reference-quality data and comprehensive corrections for systematic errors. This rigorous framework justifies their application as the definitive ‘referent truth’ benchmark for remote sensing validation. RMSE and bias were the primary metrics used to assess relative accuracy. GPS measurements provided the highest level of relative accuracy, yielding the lowest RMSE (1.50 mm) and a near-unity linear fit (y = 0.98x). NOAA-21 and TROPOMI exhibit higher random noise when compared to ground-based instrumentation, yet both obtain high relative accuracy retrievals with negligible biases. AERONET and Pandora also showed strong performance with low RMSEs and R² values of 0.986 and 0.988, respectively, while slightly underestimating PWV. In contrast, the Radiometer performed with the lowest relative accuracy, characterized by the highest RMSE (6.19 mm) and a significant negative bias (−5.55 mm). Although all instruments maintained high correlation coefficients (R² ≥ 0.904), these results indicate that satellite and ground-based remote sensing provide reliable PWV retrievals, while GPS presents the most robust benchmark for high-accuracy PWV retrievals relative to radiosonde measurements. The findings also underscore the need for instrument-specific calibration constants to better align remote sensing retrievals with in situ observations. Full article

Airborne microplastics (AMPs) are increasingly recognized as an emerging air pollutant. However, observational data remain scarce in Southeast Asia. This study provides the first observations of AMPs in Phnom Penh, Cambodia, using µFTIR-ATR imaging. Number concentration, morphology, polymer composition, aerodynamic size distribution, Feret diameter, and surface aging characteristics were investigated together with meteorological parameters, gaseous pollutants, water-soluble ionic tracers, and HYSPLIT backward trajectories to examine possible source attribution. AMPs were dominated by polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET), with 52% classified as fragments and 82% having Feret diameter smaller than 30 µm. Across four independent 72-h sampling periods (n = 4), AMP concentrations ranged from 0.55 to 1.27 MP m⁻³ in TSP, with a mean ± standard deviation of 0.97 ± 0.30 MP m⁻³, and from 0.23 to 0.49 MP m⁻³ in the PM_2.5 fraction, with a mean ± standard deviation of 0.33 ± 0.10 MP m⁻³. In total, 134 particles were identified in TSP, of which 46 were detected in the PM_2.5 fraction. Carbonyl and hydroxyl indices indicated that PE and PP were relatively fresh and in low-to-moderate surface aging states. Pearson correlations suggested that the abundances of individual polymers were varied differently in relation to local environmental and precipitation-related variables; however, the limited number of sampling periods precludes source or process attribution. In addition, HYSPLIT backward trajectories showed that some air masses arriving in Phnom Penh had passed over marine regions under southwest monsoon flow. These findings provide the first baseline dataset for AMP pollution in Phnom Penh, Cambodia, and highlight the combined importance of local emissions and regional atmospheric transport in Southeast Asia. Full article

El Niño–Southern Oscillation (ENSO) modulates tropical South American rainfall, but its Colombian expression is filtered by terrain, rainfall regime, moisture pathways, and atmospheric state. We quantify ENSO-related sensitivity of standardized precipitation anomalies in the Colombian Andes and Orinoquia using Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS v2.0; 1981–February 2026), station records from Colombia’s Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM), ERA5 atmospheric fields, and 1981–2010 climatologies. CHIRPS reproduced station-derived standardized anomalies ($r=0.94$ in the Andes; $r=0.91$ in Orinoquia), supporting regional anomaly analysis while retaining cautious comparison framing. Lagged associations with the Oceanic Niño Index (ONI) were evaluated for lags 0–6 months using effective sample size, block-bootstrap confidence intervals, and maximum-lag tests. ENSO sensitivity was stronger and more coherent in the Andes: annual lag-1 ONI–precipitation correlation was $-0.374$, with marked December–February and June–August responses. El Niño minus La Niña composites of column water vapor, 850-hPa moisture-flux convergence, 500-hPa vertical velocity, and Convective Available Potential Energy (CAPE) revealed seasonally heterogeneous moisture and convergence responses, but coherent positive $\omega$ anomalies over the Andes in DJF and JJA, consistent with reduced ascent. CAPE was significantly higher in MAM–SON, whereas the positive DJF difference was not statistically significant, showing that thermodynamic instability alone did not determine rainfall. Orinoquia did not exhibit a comparably consistent four-variable atmospheric signature. An elevation-stratified analysis showed a modest lowland-to-upland strengthening that plateaued above approximately 1000 m. A strictly antecedent ONI-lag model retained modest fixed-split skill in the Andes ($R^2=0.138$) but negligible skill in Orinoquia ($R^2=0.003$). The results support regional diagnosis, not causal or operational claims. Full article

The decline in groundwater storage (GWS) poses a critical threat to water security in semi-arid regions where increasing agricultural water demand and climate variability are increasing pressure on aquifers. This study presents a novel hybrid modeling framework integrating multi-source satellite and climate data (GRACE, GLDAS, TerraClimate, and MODIS) with machine learning and explanatory artificial intelligence techniques for the long-term assessment and interpretation of GWS anomalies in the data-poor Iğdır Basin. Three different modeling approaches were developed: XGBoost, Long Short-Term Memory (LSTM) networks, and their combined model, and interpreted using the Shapley Additive Explanations (SHAP) method. The results showed a significant long-term decreasing trend in groundwater storage anomalies at a rate of −0.87 mm per month during the 2002–2016 period, indicating continuous depletion. The LSTM model demonstrated the best performance with R² of 0.59, RMSE of 19.5 mm, and MAE of 15.1 mm, revealing the dominant role of temporal dependencies in groundwater systems. SHAP analysis identified lagged groundwater anomalies (especially GWS_lag3) as the most effective predictors; this may reflect the memory effect and lagged response specific to semi-arid aquifer systems, but this interpretation needs to be validated in different study areas. Snow water equivalent and total water storage anomalies also emerged as significant determinants, while the direct effect of instantaneous precipitation was found to be limited. This study addresses significant gaps in the literature by combining sequence-based modeling with model interpretability in a semi-arid closed basin. The findings highlight the necessity of using system memory and explainable artificial intelligence together for reliable groundwater prediction. While the proposed hybrid approach has the potential for application in other semi-arid regions, its broader usability needs to be supported by independent validation studies under different hydrogeological and climatic conditions. Full article

Climate change is driving hydroclimatic changes that are not fully captured by conventional trend analyses. This study presents a multidimensional assessment of hydroclimatic changes in Northern Cyprus using observational records from 27 precipitation stations and 12 temperature stations, with precipitation records spanning 35–46 years and temperature records spanning 21–30 years. Modified Mann–Kendall (MMK), Pettitt (PT), Innovative Trend Analysis (ITA), and Structural Trend and Variability Identification (STVI) methods were integrated to examine monotonic trends, abrupt shifts, distribution-dependent changes, and mean–variability interactions at annual and seasonal scales. Results revealed pronounced spatial heterogeneity and seasonal asymmetry in precipitation totals and their temporal evolution. Increasing tendencies were mainly concentrated in the Kyrenia mountainous region and parts of the western coast, whereas several eastern coastal stations showed drying tendencies, particularly in spring. Winter exhibited the most coherent wetting signal, while spring was more fragmented and drying-dominated. Monthly mean of daily maximum temperature (Tmax) and monthly mean of daily minimum temperature (Tmin) generally showed widespread warming, although Tmin responses were more localized and season-dependent. ITA indicated asymmetric precipitation behavior, with medium and high precipitation values generally increasing, while low values often decreased or showed mixed responses. STVI further revealed that precipitation changes involved substantial restructuring of both mean and variability components, whereas temperature changes were mainly mean-driven. The findings provide a more comprehensive understanding of evolving hydroclimatic conditions, which can support climate adaptation and water resources management in semi-arid Mediterranean regions. Full article

Balancing terrestrial carbon-storage conservation with land development is a central sustainability challenge in transboundary river basins, yet the consequences of alternative land-use pathways in the Chinese portion of the Tumen River Basin remain insufficiently understood. Using land-use datasets from 1990, 2000, 2010, and 2020, the InVEST and PLUS models were employed to quantify historical carbon-storage change and projected land-use and carbon-storage outcomes under three scenarios for 2050. Separately, an XGBoost–SHAP framework was applied to the 2020 spatial data to interpret the spatial heterogeneity of carbon storage and identify nonlinear thresholds. Forest remained the dominant land-use type between 1990 and 2020, whereas the area of Built-up expanded by 78.7%. Over the same period, total carbon storage decreased from 153.07 × 10⁸ t to 151.03 × 10⁸ t, following a decline–recovery–decline trajectory. Among the three 2050 pathways, only the Ecological protection scenario produced a net increase in carbon storage relative to 2020 (+3.82 × 10⁶ t), whereas the Urban development scenario resulted in the largest loss (−1.65 × 10⁸ t). For the 2020 spatial pattern, the XGBoost–SHAP analysis identified NDVI and slope as the leading explanatory variables, with model-derived thresholds for slope (6.18°), precipitation (666.98 mm), NDVI (0.92), elevation (442.65 m), GDP (CNY 8725), and distance to railways (10,379.04 m). These results therefore support a spatially differentiated land-use strategy that prioritizes forest-patch continuity and restricts the replacement of carbon-dense land by Built-up within the basin. Full article

Drought is a natural hazard characterized by gradual onset and prolonged precipitation deficit. With climate change intensifying precipitation variability, accurate drought assessment is critical for effective water resource management and mitigation. Focusing on Maryland in the Mid-Atlantic region of the United States, this study computes and analyzes drought indices to assess both near (2021–2060) and late (2061–2100) drought conditions, in the context of climate variability. We employed three distinct objectives to enhance drought assessment and monitoring capabilities under projected climate scenarios: (1) calculation of the Standardized Precipitation Index (SPI) reflecting meteorological conditions using precipitation data from seven GCMs across three SSPs for two future periods (2021–2060 and 2061–2100); (2) integration of both precipitation and temperature projections in the Palmer Drought Severity Index (PDSI) (implemented here as a simplified PDSI based on a standardized Z-index) to reflect combined hydrological and thermal influences (i.e., Hydrological indices); and (3) a Low Precipitation Duration–Frequency Analysis (LPDF) as indicator of both meteorological and hydrological conditions to quantify and compare the frequency and severity of low precipitation events across different SSPs. These objectives were achieved by fitting a gamma distribution for SPI and an Extreme Value Type I distribution for LPDF, and applying Z-index (i.e., long-term moisture abnormalities) and weighting factors representing the ratio of precipitation to evapotranspiration. Results reveal notable variability in SPI values, with a general trend toward increased extreme wet conditions, especially under high emission scenarios (i.e., SSP585) in the latter half of the century (2061–2100). Meanwhile, PDSI analysis indicated a subtle shift toward drier conditions despite increases in precipitation, particularly under SSP126 and SSP245, suggesting that temperature rises may offset precipitation gains. In addition, LPDF values indicated a reduced frequency of prolonged low-precipitation events under SSP585 compared to SSP126 and SSP245; this reflects higher total precipitation and should not be interpreted as resilience to drought, since the concurrent rise in temperature-driven evaporative demand can still intensify hydrological and agricultural drought stress. These results highlight the importance of incorporating climatic variables in drought assessments to understand future meteorological and hydrological scenarios under climate change projections. The study can help water resource managers and illustrates how such integrations can enhance our understanding of future drought scenarios under different climate change projections. Full article

Objective: Hainan is an important natural rubber planting area in China. Due to topographical influences, rubber plantations in Hainan exhibit a distinct east–west distribution. The factors driving the differences in latex yield between the eastern and western regions remain unclear. This study aims to quantify the effects of climatic and soil factors on rubber yield in these regions, identify key factors, and provide a scientific basis for developing region-specific rubber plantation management strategies. Method: This study is based on 409 rubber plantation samples collected over a continuous 15-year period in Hainan Province (eastern region (n = 252) and western region (n = 157)). We analyzed regional differences in multiple indicators including soil pH, organic matter (OM), alkali-hydrolyzable nitrogen, available phosphorus, and available potassium, and core climatic variables (mean annual temperature, MAT; mean annual precipitation, MAP) between eastern and western Hainan. We further explored the relationships between these factors and rubber latex yield and identified the key yield-limiting factors for rubber plantations in different regions. Result: The yield per plant in the eastern region (3.38 kg) was significantly higher than that in the western region (3.25 kg). In the eastern region, annual precipitation (1707.78 mm), soil organic matter (24.43 g kg⁻¹), alkali-hydrolyzable nitrogen (73.64 mg kg⁻¹), and available potassium (42.73 mg kg⁻¹) were all significantly higher in the eastern region than in the western region (1604.76 mm, 15.14 g kg⁻¹, 60.75 mg kg⁻¹, and 24.55 mg kg⁻¹, respectively); while the annual mean temperature (24.02 °C) and soil pH were significantly lower than in the western region (24.17 °C, 4.81). Univariate quadratic regression revealed that both soil OM and AP exhibited significant positive correlations with rubber yield in western rubber plantations. However, after simultaneously controlling the joint variation of all climate and soil variables via multivariate stepwise regression, only OM was retained in the final predictive model. Conclusions: The key factors influencing rubber yield vary by region, with soil pH being the primary factor in the eastern region and organic matter in the western region. We explicitly differentiate two effects: short-term regional yield variations are mainly controlled by soil spatial heterogeneity rather than current climate differences, while long-term climate drives spatial divergence in soil fertility via pedogenic processes. The differences in key limiting factors between the eastern and western regions identified in this study provide data support and a scientific basis for formulating precise soil management plans for rubber plantations in Hainan Province, thereby promoting cost savings, efficiency gains, and sustainable development in Hainan’s rubber industry. Full article

Climate change is increasing the frequency and intensity of extreme rainfall events, profoundly affecting vegetation–atmosphere–soil interactions and ecosystem stability. Northeast China (NEC), a major ecological region, is highly sensitive to precipitation variability. However, the annual mechanisms underlying vegetation responses to rainfall extremes, the mediating roles of soil moisture (SM) and vapor pressure deficit (VPD), and the ecosystem-specific differences remain insufficiently understood. This study investigates these processes during 2000–2022 by integrating precipitation extremes, normalized difference vegetation index (NDVI), SM, VPD, and land cover data. Ten rainfall extreme indices were evaluated using the Mann–Kendall (MK) test and Sen’s slope estimator, while NDVI responses were examined through correlation analysis, mixed-effects models, and structural equation modeling (SEM). Results show strong spatial heterogeneity in precipitation extremes, with intensified heavy rainfall in southern NEC and prolonged drought conditions in northern areas. Vegetation exhibited significant greening trends (NDVI slope = 0.0026 yr⁻¹, R² = 0.718, p < 0.001), accompanied by increasing SM (slope = 0.0478 yr⁻¹, p = 0.003) and mild warming (slope = 0.0005 yr⁻¹, p = 0.045). NDVI showed a strong correlation with SM (ρ = 0.65, p < 0.01) but a weak relationship with temperature (ρ = 0.04, p > 0.05), highlighting SM as the dominant driver of regional greening. Grasslands and cultivated lands were more sensitive to rainfall fluctuations, whereas forests showed greater resilience. SEM results indicate that extreme rainfall affects NDVI mainly through indirect pathways mediated by SM and VPD, with mediation effects exceeding 97%. These findings improve understanding of nonlinear vegetation–atmosphere–land interactions and provide scientific insights for climate adaptation, ecosystem management, and ecological restoration under future climate change. Full article