Drought is one of the most disruptive climate extremes for agriculture, and concurrent droughts across multiple breadbasket regions can seriously threaten food-system stability under climate change. This risk is particularly important in China, a major agricultural producer with diverse crop-growing regions. Here, we assess concurrent drought risk across 14 rice, wheat and maize production regions in China during 1951-2100 using the Standardized Precipitation Evapotranspiration Index (SPEI), based on simulations from 16 CMIP6 Global Climate Models (GCMs) under three Shared Socioeconomic Pathway scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5). Results show that growing-season SPEI provides a more relevant indicator of drought risk in crop production regions than annual SPEI. All scenarios project increased drought frequency and severity in the late 21st century (2071-2100), with stronger intensification in southern and western China and a shift from moderate to severe drought, particularly in rice production regions. The probability of concurrent moderate and severe droughts among region pairs is low during the historical period (1951-1980) but increases substantially under future scenarios, especially under SSP5-8.5, with the largest increase occurring in rice production regions. The probability of concurrent drought affecting at least half of 14 crop production regions rises from 17.08% to 36.04% for moderate drought and from 4.58% to 24.58% for severe drought under SSP5-8.5. These findings improve understanding of climate change impacts on concurrent agricultural drought risk in China and provide a basis for region-specific adaptation and food security planning.

Evapotranspiration (ET) is a key hydrological and meteorological variable, serving as the critical nexus between water and energy exchanges. However, accurate estimation of global ET remains a challenging task, as process-based ET algorithms are often inadequate to capture the nonlinear relationship among environmental factors, and the application of data-driven ET algorithms is hindered by sparse and uncertain ET observations. In this study, we developed a novel ensemble framework that integrates three existing ET models (process-based algorithm, machine learning-based ET model, and hybrid model), aiming to provide high-precision terrestrial ET estimates. The framework is guided by an additional classifier that can achieve dynamic per-pixel model selection, thus fully utilizing the spatiotemporal dynamics of each model's distinct advantages in mapping global ET and avoiding the typical underestimation of high values by ensemble methods. Comprehensive validation of the model was carried out using in-situ ET observations from the FLUXNET2015 dataset, catchment-scale water balance ET dataset, and six global-scale ET products, including comparisons to individual base models and another Attention-Based ensemble model. The quantitative comparisons across statistical metrics (RMSE, MAE, R2, KGE) indicate that our ensemble model outperforms other evaluated models, especially in extreme samples. Meanwhile, the introduction of classifier can not only significantly enhance the algorithmic robustness and generalizability, but also allow us to gain a basic understanding of the mechanisms behind model selection by interpretability analysis. The study demonstrated the effectiveness of the proposed framework in enhancing ET estimation robustness, thereby providing a valuable reference for the estimation of other similar variables.

Study region A typical urban residential compound in China. Equipped with an extensive underground parking. With considerable green space area providing potential benefits of runoff mitigation via imperviousness disconnection. Study focus This study focuses on the impacts of the underground parking structure on the hydrological processes and the green space functions. Using a fully-distributed and coupled surface-subsurface model, we analyzed the changes in soil saturation, runoff, and evapotranspiration caused by the underground structure. New hydrological insights for the region Results show that the underground parking structure causes soil water accumulation on its top in long term, leading to higher runoff and evapotranspiration. The magnitude of these effects relies on water supply to the green space. Lower water supply (e.g., when the climate is dryer or soil permeability is lower) would result in less soil water accumulation, thereby weakening the hydrological effects. In contrast, these effects are amplified when imperviousness disconnection is implemented, as it increases water supply to the green space through focused infiltration. Moreover, for the condition of imperviousness disconnection, these effects are not confined to the disconnection point, as the water accumulation zone can develop via subsurface lateral preferential flow along the underground parking slab, potentially influencing runoff generation patterns over a larger area. These findings highlight the importance of considering urban underground structure in stormwater management, especially for regions with wet climate.

Widespread vegetation greening enhances terrestrial evapotranspiration (ET) by increasing evaporative surfaces, yet their dynamics and the underlying ecological mechanisms under projected warming scenarios remain unclear. Here, we quantify changes in the sensitivity of ET to growing-season averaged leaf area index (partial derivative ET/partial derivative LAI) through 2100 across emission scenarios by integrating satellite retrievals and CMIP6 Earth system model simulations within a machine-learning framework. Results show that the current positive partial derivative ET/partial derivative LAI (71.53 +/- 12.01 mm m2m-2) is projected to weaken over about 80% of global land, driven by a declining vegetation control on transpiration, particularly in warm regions and under higher-emission scenarios (-0.121 +/- 0.026 and -1.247 +/- 0.075 mm m2m-2 decade-1 under SSP1-2.6 and SSP5-8.5, respectively). Conductance-based diagnostics indicate that the suppressing effect of reduction in stomatal conductance increasingly outweighs the positive CO2 fertilization effect with the elevated CO2 concentration. Consequently, the LAI-driven evaporative cooling is expected to diminish under future conditions for the benefit of enhanced water conservation.

The net physiological effect of rising atmospheric carbon dioxide (aCO2) on terrestrial evaporation (ET) is highly uncertain. While increased CO2 fertilization elevates ET through more biomass production, the reduction in stomatal conductance (gs) that it downregulates ET. Here, using satellite-based estimates of ET and dynamic vegetation models, we investigate the physiological influence of aCO2 on ET, and isolate the respective contribution of biomass increase and gs reduction. Our results indicate that the CO2 fertilization had a net negative effect of –4.4±0.3×10–2 mm ppm–1 on ET over 1982–2018. The negative physiological effect tends to intensify with increasing aCO2, particularly in warm and humid forests. The high sensitivity of ET to gs may attenuate the expected water cycle acceleration over land, although the future evolution of these two competing physiological processes remains uncertain.

Reliable precipitation data is fundamental for accurate drought monitoring and understanding its impacts on terrestrial ecosystems. While multi-source precipitation fusion techniques show promise, a comprehensive evaluation of their performance across varying drought intensities and ecological impacts remains limited. This study evaluated two fused precipitation products—Bayesian Model Averaging Ensemble Precipitation (BMAEP) and Cheng-Kling-Gupta Efficiency Weighted-Ensemble Precipitation (CWEP)—against the Multi-Source Weighted-Ensemble Precipitation (MSWEP) across mainland China (2001–2018). We assessed their capability in monitoring droughts using 3-month Standardized Precipitation Index (SPI3) thresholds (-0.5 to −2.0) and quantified input data contributions. Furthermore, an Extreme Gradient Boosting (XGBoost) model, interpreted via SHapley Additive exPlanations (SHAP), uncovered the response patterns of vegetation (excluding irrigated croplands) to different drought intensities. Results show that fused products, particularly CWEP-2P and BMAEP-2P (both combining the Climate Prediction Center (CPC) and Modern-Era Retrospective Analysis for Research and Applications V2 (MERRA2) datasets), outperformed individual inputs and the benchmark in drought detection. CPC and MERRA2 were identified as the most critical input sources. SHAP analysis revealed that sparse vegetation and rain-fed croplands were highly susceptible to mild and moderate droughts, whereas forests exhibited more profound responses to extreme droughts (SPI ≤ −2.0), likely due to legacy effects. The fused product CWEP-2P effectively captured these ecological response patterns. The findings provide a scientific basis for selecting precipitation inputs and demonstrate the utility of interpretable machine learning in elucidating complex ecosystem responses to climate extremes, offering valuable insights for environmental management.

Climate extremes exert detrimental influences on the water retention capacity and carbon sequestration functions of forest ecosystems. However, the response mechanisms of carbon-water cycles and their coupling relationships to climate extremes remain unclear. To address these issues, we investigated in an evergreen forest ecosystem located in Southern China, with comprehensive datasets and machine learning (ML) algorithms, where water-use efficiency (WUE) is defined as the ratio of gross primary production (GPP) to evapotranspiration (ET). The spatial-temporal distribution characteristics of GPP, ET, and WUE, as well as their responses to observed extreme weather events (torrential rains, drought, heat wave and cold wave) and climate extreme indices (CEIs) during 1979-2017 were investigated. We evaluated the accuracy of 8 types of ML algorithms in modelling observed GPP and ET, and the extreme gradient boosting model showed the best performance (GPP: R-2 = 0.91-0.98, ET: R-2 = 0.84-0.98). Regionally averaged annual values indicated significant increasing (p < 0.01) trends for GPP (3.28 gC m(-2) a(-2)), ET (0.62 mm a(-2)), and WUE (0.0023 gC m(-2) mm(-1) a(-1)), with mean annual values exhibiting a gradient increase from the northwest to southeast. Across the 21 CEIs, the precipitation-related indices showed positive correlations with GPP and ET, and the warm (cold)-related temperature indices showed positive (negative) correlations with these fluxes. These fluxes were more sensitive to torrential rains and cold wave, during which the response of ET was more immediate, while the negative impact of cold wave on GPP gradually intensified over time. As a result, WUE initially remained stable but then declined sharply during cold period. Overall, the carbon-water fluxes for evergreen forests on Southern China were most likely to exhibit high risk during cold events. Our findings provide valuable references for the response of evergreen forests to climate extremes.

The Three Gorges Dam (TGD) and its impoundment significantly alter natural river properties and local land cover, drawing considerable concerns regarding its climatic and environmental effects. However, with the role of the Three Gorges Reservoir (TGR) in narrowing temperature ranges and changing precipitation patterns is well understood, its impact on moisture recycling is little known. Here, we tracked precipitation in the TGR basin back to evaporated moisture to explore the features of moisture recycling and quantify local evaporation ratios in the pre‐dam (1980–2002) and post‐dam (2003–2022) periods. The influences of the forcing data, simulation time steps and different tracking models on evaporation recycling are investigated. Relevant mechanisms are analyzed in terms of atmospheric motion, surface radiation, land cover changes and climate variability impacts. Results indicate that the precipitationshed shows a reduction in both summer and winter during the post‐dam period. Local evaporation recycling ratios (ERRs) in TGR basin decrease by 0.46%, 1.07%, 0.59, 0.94% during the post‐TGD period relative to the pre‐TGD period in spring, summer, autumn and winter, respectively. Local evaporation contributions are limited in both the pre‐dam and post‐dam periods, especially in dry years. The reduced precipitation in TGR region is more dependent on upwind moisture, which results from the enhanced sinking motion and moisture divergence. Although different forcing data and simulation time steps show good agreement in spatial and temporal variations in the recycled moisture, the local ERRs are larger when calculated from the UTrack model than from the WAM‐2layers model.

Remarkable vegetation greening has been observed in the Yangtze River Basin (YRB) during the past two decades, triggering noteworthy hydrological consequences. Previous studies have assessed the hydrological effect of vegetation greening but ignored the vegetation‐precipitation feedbacks from land‐atmosphere interactions. To address this knowledge gap, here we conduct coupled land‐atmosphere model simulations prescribed with satellite vegetation observations to investigate how vegetation greening in the YRB affects regional hydrological cycles through vegetation physiological processes and biophysical feedbacks, with potentially competing effects on water yield (WY) by altering evapotranspiration (ET) and precipitation. Over the 2001–2020 period, the leaf area index in summer shows a significant increasing trend at a rate of 0.34 m 2 m −2 decade −1 ( P < 0.01). This vegetation greening causes a substantial rise in ET, primarily due to increased plant transpiration and canopy evaporation, along with reduced soil evaporation attributed to enhanced root water uptake and shading of the soil surface. Moreover, the modeled results indicate that vegetation greening is the key driver for the observed ET enhancement. In addition, vegetation greening induces increases in precipitation by modulating moisture flux convergence, which although statistically insignificant, provides considerable water to compensate for the enhanced ET. For the cumulative effects of vegetation greening from 2001 to 2020 at the basin scale, the increased precipitation (approximately, 101 mm) outpaces the increased water consumption (approximately, 93 mm), resulting in an insignificant effect on WY. Our findings underscore the importance of considering vegetation‐precipitation feedbacks in evaluations of the hydrological response to natural or deliberate vegetation changes.

This study investigated the impact of coupling the conceptual hydrological model(GR4J)with the long short-term mem-ory model(LSTM)in a physics-informed machine learning(PIML)framework for runoff simulation.Three scenarios(Hl,H2 and H3)were designed to examine the effects of the physical model parameter feedback mechanism,the consideration of soil mois-ture as an intermediate variable,and the former both on the PIML models,respectively.The case study was conducted in the upper Han River Basin,with the Ankang hydrological station as the control station.The main findings were as follows:(1)Compared with the LSTM model,all three PIML models had improved performance on runoff simulation,with a 10.6％increase in average Nash-Sutcliffe efficiency(NSE)during the validation period.Additionally,both the PIML-H1 and PIML-H3 models exhibited bet-ter performance than the GR4J model,with a 4.2％increase in average NSE during the validation period.Notably,the PIML-H3 model outperformed other PIML models,indicating that coupling GR4J and LSTM models simultaneously considering intermediate variables and parameter feedback yielded the most significant improvement in the model performance of runoff simulation.(2)For low flows,all three PIML models outperformed the GR4J and LSTM models,and the PIML-H3 model achieved the best perform-ance.For high flows,the performance of all three PIML models was not high,implying that PIML models were suitable in simula-ting low flows events.(3)The runoff simulations from the three PIML models exhibited significantly seasonal variations during both the training and validation periods.The seasonal variations in the PIML-H2 and PIML-H3 models were more pronounced compared to that in the PIML-H1 model,indicating that the seasonal variations in simulated runoff results of the PIML model were influenced by intermediate variables.This study contributed to a better understanding of the performance differences among various PIML mod-el schemes in runoff simulation,providing technical support for runoff simulation and forecasting in the study area.

The Three Gorges Dam, the world's largest hydropower project, and its impoundment reservoir have notably modified land cover, with potential implications for regional hydroclimate. However, the seasonal dynamic climate feedbacks arising from variations in water body areas managed by the Three Gorges Reservoir (TGR) remains poorly understood. Based on data-driven analysis and regional climate simulations, we depict the impact of the TGR regulation activities on local land surface temperature (LST) and biophysical processes across different spatiotemporal dimensions, determine the spreading extent of this effect to external territories, and further identify the quantitative attributions between regional climate variabilities and the TGR operation. Results indicate that the TGR induces more pronounced daytime cooling from May to October, particularly in June-August (JJA) with -2.41±0.23 K. The influence of TGR on nighttime LST transitions to warming effects in most regions from November to April (NDJFMA). The significantly increased latent heat (LH) from evaporation growth dominates cooling effects, particularly during daytime, while in JJA, the effects of evaporation are constrained to some extent by abundant precipitation. Albedo exerts a comparatively significant dominance on the nighttime LST in NDJFMA. The TGR-induced surroundings LST changes are notably discernible within an approximately 10 km buffer. The simulations amplify the magnitude and extent of the TGR cooling effect. The simulation results reveal significant reductions in LST of 6.08% (-1.42 K, JJA) and 4.58% (-1.04 K, December-January-February, DJF). respectively, TGR-induced LH variations are dominant for cooling (contributions: -52.09% in JJA; -71.98% in DJF, respectively) among the diverse energy components. This study is valuable for providing scientific guidance in reservoir planning under changing climate.

Large artificial reservoirs have been increasingly promoted over the last decades as an effective tool to relieve the shortage of water resources and mitigate the unavoidable negative effects of climate change. However, due to complex interactions between human and natural systems, their associated biophysical effects and the spatial extent of their propagation remain unclear. Here, we focus on the Three Gorges Reservoir (TGR), the world's largest hydropower project, to quantify its induced biophysical impact over the operational period 2010-2021 by integrating ground observations, satellite-based retrievals and process-based model simulations. Results show that the impoundment of TGR has led to a cooling at the local scale in daytime land surface temperature (LST) (-1.28 +/- 0.05 K) particularly pronounced during hot and wet seasons and a warming signal in nighttime LST (+0.22 +/- 0.06 K), markedly in cold and dry seasons. Such effects propagate toward surrounding territories up to 12 km far from the water body. Model simulations suggest that the widespread alteration in land-covers and water storage resulting from the TGR has influenced considerably the surface energy budget at regional scale both in terms of energy redistribution and radiative forcing ultimately leading to a net cooling. Such signal appears mostly driven by the increase in latent heat promoted by the enhanced water availability and changes in wind fields which ultimately offsets the opposite warming effect associated with the increased solar energy absorption resulting from the reduction in surface albedo. These findings underscore the role of reservoirs in regional climate change and offer strategic insights for large dam planning to mitigate emerging, warming-related, climate threats.

Tibetan Plateau (TP), bordering major freshwater reservoirs in Asia, is facing rapid climate warming, which could significantly alter the subsurface hydrological processes that concurrently reduce regional terrestrial water storage (TWS). The present study posits a critical analysis and discussion over the impact of climate changedriven altered water flow pathways that exacerbate water stress conditions in TP. Subsequently, the variability in precipitation patterns, glacier and snow cover expansion, surface and subsurface water dynamics of TP is analyzed and discussed. The changes in TWS components that derive water stress conditions in TP are comprehensively discussed. Furthermore, key challenges, perspectives, and future research trends are explored to develop potential mitigation measures. The results reveal that precipitation has apparently decreased in the southeast TP and contrarily increased in the headwater region of Yellow River. The solid water (snow and glaciers) melting and permafrost thawing have irreversibly declined, leading to significant changes in the stream flow of major river basins in time and space since 1998. Currently,the outer area of Yellow, Ganges-Brahmaputra, Indus, and Amu Darya basins are experiencing severe water stress. It is projected that the water stress index value would increase all across Ganges-Brahmaputra (similar to 0.79 i.e. severe water stress) and Yellow basins (similar to 0.96) by 2050s and 2080s, respectively if the current rate of climate change remains unchanged. This study will reinforce in-depth understanding of climate change-driven water storage transition, which could be resourceful for developing better management practices targeted to mitigate water stress under the countenance of unstoppable climate change.

Ocean evaporation, represented by latent heat flux (LE), plays a crucial role in global precipitation patterns, water cycle dynamics, and energy exchange processes. However, existing bulk methods for quantifying ocean evaporation are associated with considerable uncertainties. The maximum entropy production (MEP) theory provides a novel framework for estimating surface heat fluxes, but its application over ocean surfaces remains largely unvalidated. Given the substantial heat storage capacity of the deep ocean, which can create temporal mismatches between variations in heat fluxes and radiation, it is crucial to account for heat storage when estimating heat fluxes. This study derived global ocean heat fluxes using the MEP theory, incorporating the effects of heat storage and adjustments to the Bowen ratio (the ratio of sensible heat to latent heat). We utilized multi-source data from seven auxiliary turbulent flux datasets and 129 globally distributed buoy stations to refine and validate the MEP model. The model was first evaluated using observed data from buoy stations, and the Bowen ratio formula that most effectively enhanced the model performance was identified. By incorporating the heat storage effect and adjusting the Bowen ratio within the MEP model, the accuracy of the estimated heat fluxes was significantly improved, achieving an R2 of 0.99 (regression slope: 0.97) and a root mean square error (RMSE) of 4.7 W m−2 compared to observations. The improved MEP method successfully addressed the underestimation of LE and the overestimation of sensible heat by the original model, providing new global estimates of LE at 93 W m−2 and sensible heat at 12 W m−2 for the annual average from 1988–2017. Compared to the 129 buoy stations, the MEP-derived global LE dataset achieved the highest accuracy, with a mean error (ME) of 1.3 W m−2, an RMSE of 15.9 W m−2, and a Kling–Gupta efficiency (KGE) of 0.89, outperforming four major long-term global heat flux datasets, including J-OFURO3, ERA5, MERRA-2, and OAFlux. Analysis of long-term trends revealed a significant increase in global ocean evaporation from 1988–2010 at a rate of 3.58 mm yr−1, followed by a decline at −2.18 mm yr−1 from 2010–2017. This dataset provides a new benchmark for the ocean surface energy budget and is expected to be a valuable resource for studies on global ocean warming, sea surface–atmosphere energy exchange, the water cycle, and climate change. The 0.25° monthly global ocean heat flux dataset based on the maximum entropy production method (GOHF-MEP) for 1988–2017 is publicly accessible at https://doi.org/10.6084/m9.figshare.26861767.v2 (Yang et al., 2024).

Understanding the contributions of anthropogenic climate forcings to heatwave intensification is essential for evaluating mitigation strategies. While greenhouse gas influences on temperature extremes are well established, the impacts of other anthropogenic forcings, particularly aerosols, remain inadequately characterized. Here, we quantify the distinct contributions of greenhouse gases, anthropogenic aerosols, and natural forcings to extreme heatwave metrics from the pre-industrial period. Globally, changes in the duration of heatwave events and cumulative heat are +2.77 +/- 0.85 days and +1.76 +/- 0.31 degrees C2 attributed to greenhouse gases, and -1.10 +/- 0.34 days and -0.85 +/- 0.14 degrees C2 due to anthropogenic aerosols, respectively, over the past 3 decades relative to pre-industrial levels. This indicates that aerosols substantially masked greenhouse gas effects until the 1990s. Under current mitigation policies, declining aerosol emissions have exacerbated heatwave intensification at rates of +1.07 +/- 0.32 days decade-1 and +0.47 +/- 0.09 degrees C2 decade-1 for duration and cumulative heat respectively, exceeding the intensification attributable to greenhouse gases alone. Heatwave intensification has been driven primarily by reduced cloud cover and increased shortwave radiation resulting from weakening aerosol forcing, especially in Central North America and Europe. However, the regional climate changes driven by greenhouse gases and aerosols exhibit spatial heterogeneity, highlighting the necessity for geographically targeted mitigation strategies.

Vegetation greening in China is known to cool the land surface by altering the energy budget through biophysical processes. However, its mitigation effects on extreme temperatures and the underlying mechanisms remain poorly understood. Here, we use coupled land-atmosphere model simulations to quantitatively assess the effects of vegetation greening on summer mean and extreme land surface temperatures in Eastern China over the 2003-2018 period. We show that the modeled cooling effect on summer mean land surface temperature is more pronounced in arid Northeastern China than in humid Southeastern China, consistent with satellite-derived temperature responses. In contrast, for extreme hot temperatures, the spatial pattern of the cooling effect reverses, largely because high temperatures accompanied by strong radiation can alleviate energy constraints on evaporative cooling in Southeastern China. These findings underscore the potential role of vegetation greening in mitigating extreme hot extremes, with important implications for local land-based mitigation and adaptation strategies.

Long-term, continuous, and highly accurate precipitation estimation is crucial for reliable drought monitoring. Precipitation estimation based on gauge measurements, satellite retrieval, and reanalysis datasets exhibits heterogeneous uncertainties across different regions of mainland China. This study introduces two modifiable weighting schemes (Cheng-Kling-Gupta Efficiency (CKGE) weighted-ensemble model (CWEM) and Bayesian Model Averaging (BMA)), utilizing posterior probabilities generated by BMA and the performance of CKGE, to merge 7 monthly precipitation datasets into new weighted precipitation (BMA Ensemble Precipitation (BMAEP) and CKGE Weighted-Ensemble Precipitation (CWEP)) using various quantity combination schemes. Subsequently, the precision and drought monitoring utility of the weighted precipitation are evaluated and compared with the representative fused precipitation product Multi-Source Weighted-Ensemble Precipitation (MSWEP) in mainland China using gauged data. The amalgamated results demonstrate that, compared to individual datasets, certain weighted precipitation schemes exhibit superiority over MSWEP. In particular, BMAEP-2P demonstrates superior performance in the composite index CKGE, achieving a value of 0.828. CWEP-4P exhibits a higher correlation coefficient (CC), reaching 0.905. CWEP-2P excels in terms of relative bias (BIAS) and root mean square error (RMSE), with values of 0.579 % and 20.755 mm, respectively. Furthermore, BMAEP or CWEP performs optimally in drought monitoring applications across all sub-regions of mainland China at different time scales (1, 3, 6, 12 and 24 months), with the average of the highest values of CC and probability of detection (POD) reached 0.919 and 0.844, respectively. Further contribution analysis reveals CPC as the dominant factor contributing to the greatest improvement in the performance (excluding CKGE, CC_SPEI1, CC_SPEI24 and POD_SPEI24) of the fusion models, among which it boosted MERRA2 ' s performance on POD_SPEI6 by 9.41 %. In conclusion, the merging schemes based on CWEM and BMA methods effectively generate a new precipitation dataset, integrating information from multiple products into drought monitoring applications.

Compound extreme events, such as simultaneous soil drought and atmospheric aridity (CDAEs), have garnered wide attention for their devastating effect on the terrestrial ecosystem, which is greater than the impact of individual extremes. Large-scale changes in land cover have been shown to profoundly impact water-energy fluxes and hydrometeorological processes, affecting CDAEs. However, isolating the contribution of land cover change to the occurrence of such CDAEs has not been thoroughly evaluated. Here, by analyzing the subtraction of two scenarios with only change in landcover (i.e., afforestation and non-afforestation), we isolate the performance of land cover change on the CDAEs in the summer season at the Loess Plateau (LP) over history (1850-2014) and future (2015-2100). Effected by the closed stomatal in water-limited region, afforestation weakened the interaction between soil moisture (SM) and vapor pressure deficit (VPD), thereby reducing the occurrence probability of CDAEs at the LP, especially the occurrence probability of future CDAEs has decreased by over 5% at the northern LP. Additionally, we identified the influences of specific land and atmospheric processes through afforestation and non-afforestation on LP's CDAEs in the summer. Given afforestation alters the distribution of energy and water flux, the historical decrease in CDAEs was primarily associated with the land cover change due to afforestation that resulted in the increased contributions of the leaf area index (10% contributions) and temperature cooling (13% contributions). In contrast, the CO2 levels that were influenced by land cover changes would dominate the occurrence of CDAEs in the future, with the increasing by 10.6% contributions from afforestation. Our perspective provides insight into the response of CDAEs related to land cover change, which is necessary to design adaption strategies for compound extreme events, especially in fragile ecosystems.

We employed the window search strategy to investigate the impact of afforestation on land surface temperature (LST) in the Three Gorges Reservoir Area from 2000 to 2021. The inverse distance weighting interpolation method was used to quantify the actual temperature effect of afforestation. The results showed the primary form of land use changes in the Three Gorges Reservoir Area was the conversion between woodland and cultivated land. The potential temperature effect of woodland resulted in a decrease in daytime LST reduction of (0.09±0.02) ℃, a nighttime reduction of (0.06±0.01) ℃, and an annual reduction of (0.07±0.01) ℃ on the interannual scale. The actual temperature effect of afforestation led to a daytime LST reduction of 0.05 ℃, a nighttime reduction of 0.01 ℃, and an annual average reduction of 0.02 ℃. Those results indicated that woodland in the Three Gorges Reservoir Area exhibited a cooling effect during day and night. Furthermore, the cooling effect of the potential temperature was greater than that of the actual temperature, a discrepancy primarily attributed to the differences in the assumptions and handling of afforestation intensity between potential temperature effect and the observed value.