Search Results (428)

This study systematically evaluates the performance of GPM_IMERG and GSMaP_Gauge hourly precipitation products in typhoon rainfall over Mainland China. Using hourly gauge observations from the China Meteorological Administration (CMA)’s national basic meteorological stations as reference, 32 landfalling typhoons during 2021–2025 are analyzed. A multi-layered evaluation framework is established based on 50 km annular stratification from 0 to 500 km relative to typhoon centers, multiple statistical metrics, and dual thresholds for light rain and extreme precipitation. Results indicate systematic underestimation of typhoon rainfall by both products, with GSMaP_Gauge exhibiting more severe negative bias that intensifies nonlinearly with increasing rainfall intensity. Spatially, widespread overestimation occurs in North China, while underestimation dominates elsewhere, with large negative biases concentrated in high-observation regions. Monthly variations show predominantly negative deviations across most months, with GSMaP_Gauge demonstrating persistent negative anomalies except for sporadic positive outliers. Regarding precipitation detection capability, both products perform adequately for light rain, but their capability to capture extreme precipitation remains rather limited, as evidenced by sharply declining Critical Success Index (CSI) across all distance ranges and omission of over 60% extreme precipitation events. GPM_IMERG shows only sporadic high CSI values in the inner-core region during June and October. Error distributions exhibit significant spatiotemporal non-stationarity: errors attenuate markedly within 0–100 km of typhoon centers; seasonally, June and September show higher correlation coefficients but larger RMSE, whereas August presents lower correlation yet smaller errors; diurnally, the 0–50 km zone displays a “three-peak–two-valley” pattern with error maxima in the afternoon, early morning, and evening. In conclusion, both products estimate light typhoon precipitation with reasonable accuracy but still have considerable room for improvement in estimating heavy and extreme rainfall. Dynamic error models based on three-dimensional stratification of distance–season–diurnal phase, coupled with bias correction, are imperative before their application to hydrometeorological modeling, disaster investigation, and climate research. Full article

Harnessing the powerful learning and modeling capabilities of artificial intelligence, this study introduces a deep learning-driven wind data quality control algorithm that employs correlation verification rules. By constructing a Dual-Track Information Fusion Network (DTF-Net), it captures local temporal variations in wind speed via the temporal track and uncovers physical coupling relationships among temperature, pressure, wind direction, and other variables through the global track. Integrating dynamic three-standard-deviation spike detection with 3δ-RMSE spatial validation based on deep learning predictions, the algorithm enables multi-dimensional collaborative anomaly detection in the absence of neighboring stations. Experimental findings demonstrate that the proposed method achieves Mean Absolute Errors (MAE) of 0.217, 0.398, and 0.462 for 1 h, 12 h, and 24 h wind speed forecasts, respectively, representing a 3.8–61.3% reduction compared to general-purpose models like AutoFormer, ITransformer, and FiLM. The anomaly detection rate for quality control ranges from 0.33% to 9.20%, effectively identifying data aberrations during buoy maintenance, equipment failures, and abrupt changes in short-term weather patterns. This study leverages the powerful learning and modeling capabilities of artificial intelligence to establish a novel and easily understandable intelligent quality-control paradigm for sparse ocean observation networks, providing direct practical value for improving the quality of marine meteorological data assimilation. Full article

Reliable flood simulation in large cold-region basins requires understanding how meteorological forcing differences propagate through runoff generation and river routing. We compared CMFD, GSWP3v1, and CRUNCEPv7 using a controlled, uncalibrated offline CLM5–CaMa-Flood framework for the Songhua River Basin during 1996–2014, with all non-forcing settings fixed. Evaluation included daily and monthly discharge, seasonal hydrographs, annual maximum daily discharge (AMAX), observed Q95/Q99 thresholds, selected 1998 and 2013 warm-season high-flow cases, runoff-process diagnostics, event-window sensitivity tests, 5000 paired year-wise bootstrap resamples, and auxiliary water-level anomalies. CMFD generally produced the highest r, KGE, and daily NSE, but also the largest positive long-term Bias. CRUNCEPv7 systematically underestimated discharge, whereas GSWP3v1 more often yielded the smallest absolute Bias. For both selected events, CMFD reduced peak and volume underestimation, although peaks remained smoothed and delayed. Event-window precipitation differences did not translate proportionally into CLM5 runoff, and the larger CMFD response involved increases in both surface runoff and subsurface drainage. The event-magnitude ordering remained stable across ±30-, ±45-, and ±60-day windows. Bootstrap results showed a robust CMFD advantage over GSWP3v1 for temporal agreement and efficiency, while several CMFD–CRUNCEPv7 comparisons remained sample-dependent. Forcing-product performance was therefore scale-, metric-, and target-dependent and conditional on the fixed model configuration. Full article

Developing context-specific drought regionalization is crucial for targeted risk management, as drought evolves as a cascading hazard driven by complex land–atmosphere interactions rather than isolated climatic anomalies. However, conventional regionalization frameworks remain largely static and fail to capture the dynamic propagation from meteorological forcing to agricultural impacts. To address this limitation, we developed a framework that links continuous drought dynamics to discrete drought events, enabling identification of propagation patterns and their associated environmental mechanisms across the Loess Plateau, China. By integrating run theory, dimensionality reduction, clustering, and explainable machine learning, we identified three distinct drought propagation regimes: Propagation Blocked, Disaster Amplified, and Response Desensitized zones. At the regional scale, eco-hydrological factors, particularly vegetation productivity and soil moisture dynamics, showed the strongest attribution signals for differentiating drought propagation regimes. However, regime-specific environmental associations differed substantially: (i) propagation blockage was associated with terrain–vegetation interactions; (ii) disaster amplification was associated with low ecological productivity and declining soil moisture; and (iii) response desensitization was associated with intensive agricultural activities and relatively favorable soil moisture conditions, which may partly buffer vegetation responses to thermal and meteorological stress and create apparent resilience that may mask underlying hydrological vulnerability. SHAP analysis further indicated that topography and thermal conditions were strongly associated with broad-scale differentiation, while eco-hydrological conditions showed stronger associations with local regime-specific responses. Anthropogenic activities may also be associated with altered drought propagation pathways and potential risks of unsustainable water use. These findings highlight drought as a dynamic propagation process rather than a static hazard and provide a basis for targeted drought management strategies. Full article

Accurate influenza forecasting is essential for public health emergency preparedness and timely resource allocation. Although meteorological factors are established modulators of influenza transmission, existing deep-learning models rarely exploit this physical knowledge in a principled way. We introduce MeteoGST, a meteorology-driven spatiotemporal graph learning framework that combines (i) multi-scale feature extraction across seven operational meteorological variables (T_max, T_min, DTR, absolute humidity q, relative humidity RH, surface-pressure anomaly p_anom, and 10 m wind speed U₁₀) via parallel dilated convolutions, TCN, and Transformer branches; (ii) a meteorology-aware dynamic graph attention network (MeteoGAT) whose edges blend geographic adjacency with time-varying meteorological similarity through a learned gate α; (iii) residual-trend decomposition with a peak-aware composite loss; and (iv) anti-smoothing meta-learning adaptation. On a 34-city pre-COVID-19 benchmark (2018–2019), MeteoGST achieves an RMSE of 0.65/0.85/1.12, MAE of 0.48/0.63/0.82, R² of 0.84/0.76/0.70, and Peak F1 of 0.72/0.65/0.59 at 7-, 14-, and 30-day horizons, respectively—improvements of 4–6% over the strongest GNN baseline (MPNN-LSTM) and 31–44% over classical baselines (ARIMA/LSTM). Under a 2021–2022 distribution-shift stress test, the model retains its ranking at 1- and 4-week horizons (PCC 0.78/0.62) and degrades gracefully at 8 weeks, demonstrating robustness beyond the training distribution. MeteoGST offers an operationally deployable tool (MeteoGST-Lite: ≈1 ms per city-week on a laptop-class CPU) for integrated meteorology-aware influenza surveillance. Full article

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. Full article

Terrestrial water storage in semi-arid basins is increasingly affected by the combined pressures of climate variability, land use change and intensive human activities. However, how landscape composition and configuration interact with climatic forcing to shape basin-scale terrestrial water storage anomalies (TWSA) remains insufficiently understood, particularly in rapidly urbanising tributary basins of the Yellow River. This study integrates GRACE/GRACE-FO-derived TWSA, meteorological observations and multi-period land use data to examine the coupled relationships among climate variability, landscape patterns and water storage in the human-dominated Yiluo River Basin. The results show that basin-averaged TWSA experienced a significant long-term decline during the GRACE/GRACE-FO period (−4.47 mm yr⁻¹), with a statistically detectable transition around 2012 and stronger depletion in the northern and eastern parts of the basin. Precipitation exhibited a cumulative and delayed relationship with TWSA, with the strongest raw association occurring under a six-month accumulation and one-month lag (Pearson’s r = 0.44, p < 0.001, n = 134). After removing the seasonal cycle, the relationship remained significant but weaker (r = 0.30, p = 0.001, n = 129), indicating that precipitation explains part, but not all, of the interannual variability in water storage. Landscape composition showed stronger associations with TWSA than landscape configuration. Construction land was negatively associated with water storage, whereas cultivated land and grassland showed positive associations, suggesting that impervious surface expansion and the loss of permeable land may weaken the basin’s water retention capacity. These findings indicate that water storage change in the Yiluo River Basin is shaped by both climatic forcing and human-induced land surface transformation. Basin management should therefore prioritise the control of urban impervious surface expansion, the protection of permeable agricultural and ecological land, and the integration of land use planning with adaptive water resource regulation. Full article

Lakes on the Tibetan Plateau are sensitive indicators of climate change. Their water storage variations play an important role in regional hydrological processes and ecological security. This study is based on multi-source remote sensing and meteorological data from 1988 to 2025. Lake area was extracted using the MNDWI and the Otsu threshold method. HYDROWEB water level data were used to establish an area-water level relationship. This relationship was then applied to reconstruct a long-term water level time-series and estimate changes in lake water storage. GRACE/GRACE-FO data and meteorological observations were further analyzed to identify the driving factors. The results show that Siling Co experienced a persistent expansion over the study period, with the lake area increasing by 805.83 km², water level rising by 14.33 m, and water storage increasing by 30.42 km³. Correlation analysis indicates that air temperature, precipitation, and evaporation jointly influenced lake water storage variations. Among these factors, precipitation plays a relatively more important role. During 2002–2019, lake water storage changes (LWSC) were highly consistent with terrestrial water storage (TWS) variations. However, TWS anomalies lagged approximately one year behind LWSC. These findings improve the understanding of the long-term hydrological responses of Siling Co to climate change. They also provide a scientific basis for water resource management and infrastructure planning in the region. Full article

Sea surface temperature (SST) anomalies represent a critical threat to the operational safety and productivity of marine ranching systems. Taking a typical marine ranching area in the Northern Yellow Sea as the study area, this study developed a lightweight two-stage field-to-site forecasting framework. In the first stage, a Convolutional Long Short-Term Memory network (ConvLSTM) was employed to generate 1–5 days regional SST forecasts. Through experiments involving 24 input configurations, the combination of Optimum Interpolation Sea Surface Temperature (OISST), seasonal and trend components, and ERA5 meteorological variables was identified as an optimal configuration, providing spatial-evolution constraints for the target location. In the second stage, the 5-day target-site forecasts were concatenated with OISST to construct a 60-day sequence, which was then used to drive a lightweight Gated Recurrent Unit (GRU) for extended forecasts from days 6 to 15. This strategy reformulates the forecasting task into a spatially constrained 10-day extension forecast, thereby suppressing long-lead error accumulation. The results showed that, in the regional forecasting stage, an RMSE of 0.83 °C and MAE of 0.63 °C were achieved on Day 5, while in the extended-forecasting stage, an RMSE of 0.89 °C and MAE of 0.69 °C were achieved on Day 15. Compared with single-stage ConvLSTM and GRU models performing direct 15-day forecasting, the field-to-site strategy reduced RMSE by approximately 25.21% and 9.18%, respectively, and reduced MAE by approximately 24.18% and 8.00%, respectively. Independent validation against in situ buoy observations further showed that the proposed framework introduced only limited additional errors comparable to the inherent discrepancy between OISST and buoy observations. Additional tests under representative marine heatwave (MHW) conditions showed that the framework retained useful forecasting skill under anomalous warming conditions. Furthermore, its extended application experiments at five additional marine ranching sites in the Northern Yellow Sea produced consistent forecasting performance, with mean RMSE and MAE ranging from 0.71 °C to 0.75 °C and from 0.53 °C to 0.56 °C, respectively. The proposed method can therefore provide a risk-warning window of at least two weeks for marine ranching management and support timely operational decisions for temperature-related risk mitigation and refined aquaculture management. Full article

Flash drought, an abruptly intensifying meteorological anomaly, poses a growing threat to agricultural production, ecosystem stability, and regional carbon cycling, particularly in croplands of monsoon regions. Existing studies have largely focused on point-scale identification or conventional vegetation indices, whereas the regional spatiotemporal evolution of flash droughts and crop-specific differences in photosynthetic recovery remain poorly understood. Using multi-source remote sensing data for the North China Plain and the Middle–Lower Yangtze Plain during 2001–2024, this study integrated triple-collocation error assessment, root-zone soil-moisture percentile identification, connected-component tracking, and Random Forest–SHAP analysis to characterize flash drought trajectories and their vegetation impacts. The results showed that the southern Middle–Lower Yangtze Plain exhibited a high-frequency but low-intensity pattern, whereas the central North China Plain was characterized by lower frequency yet higher intensity and longer duration. Rice-based systems were more vulnerable to frequent flash drought shocks, whereas rainfed and rotation systems faced stronger cumulative risks. Solar-induced chlorophyll fluorescence (SIF) responded to flash droughts 6–9 days earlier than gross primary productivity (GPP), and all cropping systems displayed a “rapid physiological response–lagged carbon-assimilation recovery” pattern. The month of occurrence, drought duration, and decline rate were identified as the dominant factors governing photosynthetic recovery. These findings extend the flash drought monitoring framework to incorporate regional connectivity and crop recovery mechanisms, providing a remote-sensing basis for agricultural early warning, drought mitigation, and food-security management. Full article

This study evaluates the spatial variability of hydric stress in the State of Sinaloa, northwestern Mexico, through the integrated analysis of hydroclimatic variables, multispectral remote sensing indicators, and environmental factors. Historical hydroclimatic conditions were analyzed using meteorological records from 1961 to 2020, whereas Landsat 8 imagery acquired on 7 July 2025, was used to evaluate the spatial expression of hydric stress. Reference evapotranspiration (ETo) was estimated using the FAO-56 Penman–Monteith methodology, and hydrological deficit conditions were determined from the relationship between precipitation (P) and ETo. Spectral indicators including land surface temperature ($\overline{T}$_a), the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and the NDWI/MNDWI relationship were used to evaluate vegetation response, surface moisture conditions, and thermal anomalies associated with hydric stress. The results revealed persistent conditions where ETo systematically exceeded P, with hydrological deficit values ranging from approximately −1600 mm·year⁻¹ to localized positive values near 50 mm·year⁻¹. The most severe deficits were concentrated within the northwestern and north-central agricultural valleys of Sinaloa. Statistical validation revealed significant negative relationships between hydrological deficit and all evaluated spectral indicators. The strongest association was observed for MNDWI (R² = 0.387), followed by NDWI/MNDWI (R² = 0.277), NDWI (R² = 0.220), and NDVI (R² = 0.134), confirming the sensitivity of vegetation and moisture-related indicators to long-term hydrological stress conditions. Spatial analyses revealed a strong correspondence among low NDVI, negative NDWI and MNDWI responses, elevated $\overline{T}$_a, and regions characterized by high atmospheric evaporative demand. Additional spatial validation integrating land-use and vegetation-cover changes (1993–2011), regional geology, topography, and the distribution of highly productive agricultural valleys demonstrated that the most severe hydrological deficits coincided with areas affected by vegetation-cover loss, agricultural expansion, and intensive land use. Although these datasets correspond to different observation periods, they collectively reflect the cumulative environmental effects associated with persistent hydrological stress across the region. The combined effects of hydrological imbalance, forest-cover reduction, and agricultural intensification have progressively reduced ecosystem resilience and increased environmental vulnerability throughout one of the most productive agricultural regions of northwestern Mexico. These findings provide a scientific basis for water-resource management, territorial planning, ecosystem restoration, and climate-adaptation strategies under increasing water-scarcity conditions. Full article

Climate change is profoundly altering precipitation and temperature patterns across high-altitude regions worldwide. The Qinghai–Tibet Plateau (QTP), known as the “Third Pole” and the “Asian Water Tower,” is among the most climate-sensitive regions and plays a critical role in the Asian water cycle, cryospheric stability, and regional ecological security. However, the complex topography and diverse climate of the QTP result in substantial discrepancies among meteorological products over this region, highlighting the necessity of a comprehensive evaluation against in situ observational records. Using records from 85 stations (1960–2022), we evaluated four products: China’s 1 km monthly dataset (CN_1km), the Climatic Research Unit gridded Time Series (CRU TS), the fifth-generation European Centre for Medium-Range Weather Forecasts land reanalysis (ERA5-Land), and TerraClimate—selected for their long-term continuity, diverse product types, and widespread regional applications. Subsequently, we compared these products with Earth System Model (ESM) simulations from the NASA Earth Exchange Global Daily Downscaled Projections based on CMIP6 (NEX–GDDP–CMIP6). This evaluation was conducted using key statistical metrics, including the coefficient of determination (R²), root mean square error (RMSE), Kling–Gupta efficiency (KGE), and bias, together with spatially distributed long-term trend analysis using the Sen’s slope estimator and Mann–Kendall test. Station-based evaluation shows that temperature datasets generally outperform precipitation datasets, with monthly mean temperature yielding R² values of 0.85–0.94, RMSE values of 2.38–4.79 °C, and KGE values ranging from −0.04 to 0.86. Monthly precipitation R² values of 0.74–0.81, RMSE values of 20.60–36.12 mm, and KGE values of 0.42–0.86. For anomalies, temperature performs better (R² = 0.41–0.67; RMSE = 0.80–1.41 °C) than precipitation (R² = 0.28–0.44; RMSE = 16.87–20.73 mm). Overall, CN_1km and TerraClimate provide the most reliable station-based temperature estimates, while TerraClimate shows the most robust precipitation performance. All four datasets consistently indicate warming and wetting trends, with temperature rising at 0.21–0.24 °C decade⁻¹ and precipitation increasing at 4.5–5.8 mm decade⁻¹, featuring stronger warming in the west and greater precipitation increases in the northeast; however, the precipitation trend in ERA5-Land does not reach statistical significance. NEX–GDDP–CMIP6 simulations reproduce comparable warming and moistening signals (0.22–0.23 °C decade⁻¹ and 4.1–4.7 mm decade⁻¹), though their precipitation distribution differs markedly from the other datasets, with the discrepancy primarily reflected in a pronounced latitudinal gradient. These results provide a reference for the selection of climate-forcing datasets in hydrological, ecological, and cryospheric studies across the QTP. Full article

Ozone (O₃) pollution is highly sensitive to meteorological variability and regional transport, particularly in coastal southeastern China. During April–May 2025, Xiamen experienced an atypical, persistent springtime O₃ episode substantially exceeding the 2014–2024 baseline. Using surface observations and ERA5 reanalysis data, this study investigates the meteorological drivers and formation mechanisms. At Hongwen station, the MDA8 O₃ > 160 μg m⁻³ exceedance frequency reached 11.5% (historical average: 0.1%). This anomaly was closely linked to an anomalous Western Pacific Subtropical High (WPSH) configuration, characterized by northward displacement and accompanying westward extension. Compared to historical high-pollution conditions, surface temperature and downward solar radiation increased by 2.32 °C and 51 W m⁻², while wind speed and planetary boundary layer height decreased by 15.3% and 24.2%, favoring O₃ production and precursor accumulation. Two distinct pollution periods were identified. Period 1 (29 April–1 May) featured local photochemical enhancement under stagnant conditions; regional mean NO₂ increased by 31 μg m⁻³ before the peak, indicating substantial precursor accumulation. Simultaneously, the mean nighttime O₃ concentration at the Huli site during Period 1 was 50.5 μg m⁻³ (43% lower than that at Hongwen) due to enhanced NO titration from port emissions. Period 2 (12–14 May) involved regional transport, where persistent 850-hPa southwesterly flow facilitated pollutant transport along the coastal corridor, increasing O₃ and PM_2.5 by 40 μg m⁻³ and 38 μg m⁻³. Thus, extreme springtime O₃ over southeastern coastal China resulted from anomalous large-scale circulation, regional transport, and local photochemical processes. Full article

As extreme weather events increasingly threaten global food systems, accurately assessing climate risks and predicting regional crop yields remains a critical challenge. Conventional prediction models often rely on direct weather-to-yield relationships, bypassing continuous crop physiological responses and limiting their capacity to capture fine-grained temporal impacts of meteorological anomalies. To address this, we propose a novel two-stage spatiotemporal functional framework that integrates high-resolution daily weather trajectories with satellite-derived indicators, utilizing the Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) to represent canopy structural vigor and hydraulic status, respectively. In the first stage, a Historical Functional Linear Model (HFLM) dynamically maps daily meteorological trajectories (temperature, precipitation, and solar radiation) onto continuous physiological curves under strict temporal causality constraints. This generates bivariate coefficient surfaces that reveal dynamic windows of vulnerability and capture divergent, lagged physiological responses to climate stress. In the second stage, a spatially heterogeneous functional additive model integrates these weather-shaped physiological trajectories alongside raw meteorological dynamics as joint predictors for county-level yields. By extracting functional principal components and modeling flexible non-linear biological responses while accounting for continuous spatial heterogeneity, this dual-channel frameworkcaptures key aspects of both chronic physiological stress and acute meteorological shocks. Validated across a 25-year (2000–2024) U.S. Corn Belt panel, the proposed DC-FAM achieves a mean weighted mean squared prediction error (WMSPE) of 242.33 (bu/acre)² and a median out-of-sample $R_{\mathrm{cv}}^2$ of 0.422, outperforming all benchmarks including a random forest. Attribution of the 2012 flash drought further demonstrates the framework’s capacity to mechanistically trace the complete disaster propagation chain from anomalous spring warming to mid-summer hydraulic failure. The proposed framework provides a transparent, biophysically grounded tool for decoding dynamic climate stress trajectories and disaster propagation chains, offering potential implications for adaptive farm management and precision agricultural insurance. Full article

A quantitative analysis of larch-dominated Siberian forest regions was conducted to evaluate wildfire characteristics in relation to Fire Radiative Power (FRP), long-term meteorological dynamics, and FRP range ratios. The results were validated against empirical stand mortality data spanning the period 2001–2024, obtained from the Global Forest Change dataset. Spatiotemporal burn characteristics were derived from the standard MODIS burned area product, while FRP data were extracted from the corresponding thermal anomalies product. Increasing trends in extreme FRP values were observed (4.5–17.9% of annual fire pixels), indicating that high-intensity fires progressively drive tree stand mortality statistics (R² = 0.58, p < 0.01). Seasonal anomalies of the Duff Moisture Code (DMC), surface soil and litter moisture, and the Standardized Precipitation Evapotranspiration Index (SPEI) were the primary predictors of both wildfire intensity and tree cover mortality. Spatiotemporal analysis of FRP and tree cover mortality revealed that the most pronounced positive trends were concentrated in the central and northeastern forest regions of Siberia, which also exhibit high mean FRP values. These regions also experienced intensifying drought, as evidenced by the analysis of meteorological data. Consequently, under projected regional climate change, an escalating prevalence of high-intensity forest fires is anticipated to induce severe, potentially irreversible degradation of these forest stands and ecosystems. Full article