Anthropogenic climate warming is anticipated to intensify heatwaves worldwide, posing growing risks to society and ecosystems. Yet the impacts of oceanic heatwaves (OHWs) on continental heatwaves (CHWs) remain insufficiently understood. This lack of understanding hinders the accurate prediction and effective mitigation of heatwave disasters. Here, we employ a Lagrangian tracking method and reveal that 22% of CHWs can be attributed to OHWs, 43% of heatwaves occurring in coastal regions globally can be ascribed to OHWs. We show that anthropogenic greenhouse forcing has increased the likelihood of landfalling OHWs by a factor of nine. Notably, under the Shared Socioeconomic Pathway (SSP)245 (SSP370) scenario, limiting warming to 1.5 degrees C rather than 2 degrees C above pre-industrial levels could avoid 17% (20%) landfalling OHW exposure, safeguard 21% (32%) of the population, and preserve 36% (42%) of economic status. Our findings underscore the significant role of OHWs in contributing to CHWs and emphasize the urgent benefits of stringent climate targets.

Floodplains attract disproportionate concentrations of population and economic activity globally, yet the systemic flood risks emerging from this uneven development remain insufficiently characterized. Through a global analysis spanning 2000-2020, we quantify floodplain development patterns and associated flood losses across nations with varying socio-economic statuses and flood protection levels. Our results reveal that floodplains have experienced faster growth than non-floodplains in both population density and GDP density. These trends show significant variation depending on income and flood protection levels, revealing a divergent risk pattern: wealthier, well-protected nations accumulate greater economic assets at risk, whereas poorer, under-protected nations concentrate larger populations at risk. Furthermore, the sensitivity of flood-induced economic damages to floodplain GDP growth is more pronounced in wealthier nations, whereas the sensitivity of flood-induced fatalities to floodplain population growth is higher in poorer nations. This underscores the distinct economic vulnerability of developed nations and the human vulnerability of developing nations in the face of flooding. Based on these findings, we argue for prioritizing economic resilience and the decoupling of economic development from flood-prone asset accumulation in higher-income nations, while simultaneously emphasizing humanitarian protection and equitable infrastructure investment in lower-income nations.

Water is vital for the sustainability of human society, and rational water resources management and effective adaptation strategies require a clear and holistic understanding of the drivers and uncertainties influencing water availability. However, the key factors and uncertainties, and their spatial heterogeneity in global water resources remain poorly quantified. Here, we employed optimal fingerprinting to identify the drivers of global water resources changes from 1980 to 2014. We found that greenhouse gas (GHG) forcing explains approximately 77.6 % of the observed upward trend, significantly outweighing the contributions from natural external forcing (NAT, 45.8 %) and aerosols (AER,-23.4 %). We evaluated the contributions of internal variability, model uncertainty, and scenario uncertainty to future global water-resource projections, and attributed similar to 89.2 % of the total variance to the model uncertainty. Furthermore, by integrating historical observations (1995-2014) with the emergent constraint method, we reduced uncertainties in future projections (2081-2100) under the SSP2-4.5 and SSP5-8.5 scenarios. The constrained projections revealed the underestimation of water-resource changes by 18.0 % (SSP2-4.5) and 13.4 % (SSP5-8.5) for the 2081-2100 period. Notably, model uncertainty under both warming scenarios fell by 33.1 % and 26.4 %, respectively, substantially boosting the reliability of future projections. These findings advance our understanding of the drivers and uncertainties in global water resources, informing adaptation strategies and long-term water resources planning.

Abstract Estimating daily snow water equivalent (SWE) is critical for hydrological and climate applications, yet physical models often struggle to represent SWE, especially its interannual anomalies. In this study, we developed a hybrid physics‐guided machine learning (ML) model (hybrid model), by augmenting the Community Land Model 4.0 SWE simulations with a long short‐term memory (LSTM) network. The model is trained using the GlobSnow v3.0 data set and forced with meteorological data to estimate daily SWE at 0.5° over the Northern Hemisphere (NH). Our results demonstrate that the hybrid model significantly outperforms both the standalone physical and pure ML models in predicting SWE magnitude, timing, and anomalies, especially in complex mountainous regions. Explainable ML analyses suggest that the hybrid approach leverages the snow‐related physics while effectively utilizing observational data to enhance predictive accuracy. Moreover, we identify a widespread climate memory effect influencing SWE predictions across the NH, with memory‐dominant extreme events leading to greater SWE losses or gains relative to the average impacts of all extreme events, including those without strong memory effects. These findings underscore the hybrid model's ability to correct memory‐related biases that are not fully captured in current land surface models. Overall, our study highlights the value of hybrid modeling for improving SWE simulations and its potential as an alternative snow emulator within existing land surface models.

China’s expressway network, the world’s largest, relies on engineering codes calibrated under climatic stationarity, yet the extent to which climate change has reduced the protection provided by these static design margins remains unclear. We link non-stationary precipitation extremes to expressway design thresholds, evaluate network consequences, and project future risk. Here we show that for 71.4% of expressway segments, precipitation thresholds originally set for once-in-a-century events are now exceeded more frequently, revealing a systemic adaptation gap. This gap spans all construction periods, with even segments built in the 2010s falling below their era-specific protection baseline within a decade. Network stress tests show that route redundancy can buffer localized climate stress, indicating autonomous adaptive capacity embedded in network expansion, while identified priority nodes indicate where anticipatory adaptation should begin. Future projections indicate continued widening of the gap and a northwestward shift of emerging hotspots. These findings call for dynamic design standards, climate-adjusted safety margins, and continuous risk auditing across China’s expressway network. Across 71.4% of China’s expressway segments, precipitation levels associated with their original design standards are now expected to occur more frequently than intended, revealing an adaptation gap that extends across all construction periods. By linking changing rainfall extremes with engineering thresholds, network stress tests and future climate projections, the study shows that this gap is projected to widen and that emerging hotspots are likely to shift northwestward.

Urban parks are vital for the well-being of Earth’s 4.6 billion urban residents, yet their global distribution and impact on public welfare remain poorly understood. Here we analyzed 440,000 urban parks across 1860 cities worldwide, introducing the new Comprehensive Benefit Index (CBI) to assess their richness, greenness, and accessibility, while identifying gaps in urban park construction. Our findings reveal significant global disparities: developed countries contain approximately 80% of urban parks, with high-income countries achieving an average CBI 1.64 times higher than lower-middle-income countries and 1.76 times higher than low-income countries. While upper-middle-income countries have a sufficient number of parks, they often lack greenness and accessibility. In contrast, low- and lower-middle-income countries struggle to meet the basic park availability needs of urban residents. These inequalities hinder inclusive urban development, underscoring the urgent need for targeted strategies to improve urban parks in underserved countries. Aligning with Sustainable Development Goal 11, this study offers critical insights to support sustainable urban planning and foster equitable urban park systems worldwide.

Amid accelerating climate change and intensifying human activities, flood risks have been accumulating, reinforcing the bidirectional interplay between humans and floods. Within this context, human-flood interaction has emerged as a rapidly expanding research frontier that is attracting growing attention. Here, we review 78 representative studies to examine publication trends, research hotspot shifts, and persistent challenges in this field, combining bibliometric analyses with flood disaster records. We found that, over the past three decades, publications on human-flood interaction have increased exponentially, with their focus shifting from one-way impacts towards two-way human-flood coupling. The global collaboration network displays a “dual-core” structure, with transcontinental collaboration between China and the United States and regional collaborations within Europe, whereas participation from Global South countries remains marginal. In addition, a spatial disconnect persists between the geographic distribution of research hotspot regions and that of flood records. Based on bibliometric mapping, we further highlight three enduring challenges: (i) scale mismatches, (ii) spatiotemporal coupling gaps, and (iii) deficits in behavioral modeling. The insights gained from this study will help guide future research and investment in human-flood interaction, with practical implications for enhancing resilience to flood disasters and promoting sustainable human-water coexistence.

Glacierized-catchment runoff (GR) across High Mountain Asia (HMA) is approaching peak water, but the timing is not uniform. Glacier size, elevation, and regional climate push that threshold to different points in time, with direct implications for downstream water security. We used OGGM v1.6.1, forced by bias-corrected GSWP3-W5E5 historical data and an ensemble of 13 CMIP6 GCMs under four SSP scenarios, to simulate glacier mass balance, dynamics, and glacierized-catchment runoff across 17 major HMA basins from 1940 to 2100. By 2100, HMA-wide glacier mass declines by 70.7 ± 9.6%, relative to 2000 levels, and total GR falls by 10 ± 6.5% between the early (2001–2020) and late (2081–2100) century periods. Small, low-elevation glaciers have already passed peak runoff in many basins, some before 2020, and are losing volume rapidly. Large, high-elevation glaciers continue buffering downstream flows well into the late 21st century, with basin-wide peaks as late as 2058 in Tarim and 2064 in the Amu Darya, and individual large high-elevation glacier classes peaking as late as 2083 in Tarim under high-emission scenarios. We identify three distinct peak-GR regimes: early, transitional, and delayed, driven by the size and elevation composition of glacier classes within each basin, rather than average glacier behavior. Basin-averaged projections may obscure important class-specific runoff transitions, underscoring the need for class-resolved, basin-specific water resource planning across HMA

Abstract Global warming is shifting the characteristics of snowmelt floods, while simultaneously altering flood‐generating mechanisms in cold regions. Here, we disentangle the contributions of inter‐type mechanism transitions from intra‐type characteristic shifts to overall changes in flood behavior across Northern Hemisphere snow‐dominated catchments. We demonstrate that inter‐type transitions—specifically the widespread shift from snowmelt‐ to rainfall‐driven floods—substantially alter flood rising rates and timing. While this transition has not systematically changed long‐term flood magnitudes, it has significantly steepened the rising limb of flood hydrographs. Furthermore, although rising temperatures have advanced the timing of snowmelt and rain‐on‐snow floods, the shift toward rainfall dominance has largely offset this trend, leading to a stronger synchronization between flood timing and extreme precipitation. By explicitly separating these mechanisms, our framework advances the mechanistic understanding of changing flood dynamics, offering critical insights for flood forecasting and water management.

Floodplains attract disproportionate concentrations of population and economic activity globally, yet the systemic flood risks emerging from this uneven development remain poorly characterized. Through a global analysis spanning 2000-2020, we quantify floodplain development patterns and associated flood losses across nations with varying income levels and flood protection capacities. Our results reveal that floodplains have experienced faster growth than non-floodplains in both population density and GDP density. These trends diverge sharply by income and protection levels: floodplain population density growth rates in low- and lower-middle-income countries outpaced those in high-income nations by factors of 2.33 and 7.58, respectively. Similarly, due to levee effect, regions with flood protection capacity of 100 years or more experienced GDP density growth that was 4.51 times higher than in regions with less than 10-year protection. The heightened sensitivity of flood losses to socio-economic growth stems from uneven floodplain development. This creates a divergent risk pattern: wealthier, well-protected regions accumulate greater economic assets at risk, whereas poorer, under-protected areas face the compounded burden of exposure to both population and GDP risks. Our findings highlight the urgent need for flood risk adaptation strategies that explicitly consider and address underlying floodplain socio-economic inequalities in exposure and protection.

Hot‒dry winds (HDW) and droughts are two prevailing climate hazards posing increasing threats to human societies and natural ecosystems. Insights into compound HDW and drought events (CHDWDs) fill gaps in understanding their synergistic pattern and enable more disaster prevention strategies to mitigate consequent damage. By using meteorological observations and multi-model simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we analyzed the historical spatiotemporal patterns of HDWs, droughts, and CHDWDs across China, projected their future changes under different SSP (Shared Socioeconomic Pathway) scenarios, and quantified contributions of individual events to CHDWD frequency and intensity. Findings have revealed intensified HDW severity (−0.05 per decade, p < 0.05) and drought conditions (−0.07 per decade, p < 0.01) during 1980–2022, with a strong positive correlation (r = 0.79, p < 0.01) between them. The likelihood of CHDWDs has significantly increased across China (0.093 per decade, p < 0.01), with drylands being more susceptible. Droughts exert a larger influence on CHDWDs in arid regions (77%–91% to frequency and 55%–76% to intensity), while HDWs are more influential in humid areas (41%–46% to frequency and 57%–64% to intensity). Future simulations project increased frequency and strengthened intensity of CHDWDs from 2075 to 2100 under SSP5-8.5 and SSP3-7.0. Under SSP3-7.0 and SSP1-2.6, droughts dominate CHDWD frequency (68%–87%) in most regions, while under SSP5-8.5, HDWs contribute more (54%) than droughts in humid regions. To CHDWD intensity, HDWs contribute more (53%–71%) than droughts in humid regions, and droughts contribute more (54%–83%) than HDWs in non-humid regions. These results are valuable for improving agricultural risk management and guiding the development of targeted adaptation strategies.

Accurate land cover data was fundamental for formulating sound land planning and sustainable development strategies. This study focused on the Tibetan Plateau (TP), a globally sensitive ecological area, and developed a locally tailored annual 30 m resolution land cover dataset from 1990 to 2023 (TPLCD). Leveraging the Google Earth Engine (GEE) platform for Landsat data processing, LandTrendr was employed to generate robust, high-precision training samples. Subsequently, random forest classification and spatiotemporal smoothing strategies were applied to precisely map the land cover dynamics of the TP. Rigorous validation through visual interpretation, authoritative third-party datasets (Geo-Wiki and GLCVSS), and thematic dataset cross-comparisons, revealed an overall accuracy of 84.8%, and a Kappa coefficient of 0.78, fully affirming the dataset’s high reliability. This dataset provided invaluable empirical evidence for understanding the vulnerability and adaptability of the TP’s ecosystem.

Short-term drought dynamics are critical for agricultural production and water resource management, yet the complex role of diurnal temperature range (DTR), as a key climate variable influencing surface energy and water cycles, remains poorly understood in drought processes. This study addresses this gap by integrating a high-resolution meteorological dataset (1961–2022) into a SHapley Additive exPlanations (SHAP)-based attribution framework, quantifying DTR’s impacts on short-term drought (the monthly-scale standardized precipitation-evapotranspiration index, SPEI-1) across China’s diverse climatic zones. A significant negative correlation between monthly DTR and SPEI-1 indicates that DTR directly intensifies short-term drought conditions, an effect that has strengthened significantly since 2000. DTR influences drought through dual pathways: direct exacerbation via enhanced evapotranspiration demand and indirect modulation through negative associations with precipitation and relative humidity (RH) and positive links with sunshine duration and wind speed. In arid regions, DTR interacts synergistically with precipitation and RH to exacerbate drought, whereas in humid regions, DTR’s positive association with sunshine duration partially mitigates drought severity. Importantly, DTR is identified as the primary driver of short-term drought, followed by RH and sunshine duration. A 1°C increase in DTR reduces SPEI-1 by −0.04 to −0.26 (worsening drought), while a 1°C decrease in DTR increases SPEI-1 by 0.03–0.28 (alleviating drought), which is particularly pronounced in semi-arid, arid, and hyper-arid regions. This study advances our understanding of DTR’s multifaceted role in short-term drought dynamics and highlights the urgent need for targeted adaptation strategies, such as adaptive irrigation scheduling and water resource allocation, to mitigate drought intensification, particularly in ecologically vulnerable regions.

Abstract. Multiyear droughts (MYDs) are recognized as severe drought events, with especially profound impacts on both human activities and ecosystems. However, the optimal rainfall replenishment timing (toptimal) for MYDs mitigation remains insufficiently understood. With that in mind, we conducted a retrospective analysis of historical MYDs based on the Palmer Drought Severity Index (PDSI) in China during 1961–2020, and the calibration period was set to 1961-1990. We performed a series of numerical experiments involving precipitation gradient increases for 351 selected MYDs, distributed across 199 grids (2°×2°), from 1991 to 2020, and developed a drought mitigation quantitative model (DMQM). In addition, a key coefficient (k) derived from DMQM was defined to quantify the mitigation efficiency, and toptimal was then identified as the timing corresponding to the maximum k (kmax). Overall, drought severity exhibits a nonlinear response to increased precipitation. kmax occurred most frequently in the first month of drought onset (t1), accounting for 58.79 % of all grids, while the second (t2) and third (t3) months were also non-negligible, accounting for 22.11% and 11.06 %, respectively. Compared to the humid river basins in southern China, the arid and semi-arid northern regions had a higher probability for k at t2 or t3 to exceed k at t1. Drought duration (DD) was identified as a key factor, as longer DD was associated with a greater likelihood of t2 or t3 being the toptimal, evidenced by R2 values of 0.526 and 0.578, respectively. These findings contribute to ensuring timely and regionally appropriate MYD mitigation strategies and interventions.

Vegetation physiology responses to rising atmospheric CO 2 can alter the global hydrological cycle, thereby influencing drought occurrence. It has long been controversial and poorly understood how vegetation physiological effects influence meteorological drought characteristics with increasing CO 2 . To investigate that, we employ multiple CO 2 sensitivity experiments of the state‐of‐the‐art Earth System Models (ESMs) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We quantify drought characteristics in response to rising CO 2 using two drought indices: the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), with SPEI calculated using both the Penman‐Monteith method (SPEI_PM) and energy‐only method (SPEI_Rn). Our findings reveal that plant physiological effects can robustly induce more intense, frequent, and prolonged droughts under elevated CO 2 levels. Spatially, drought intensity as measured by SPI, SPEI_PM, and SPEI_Rn, resulting from CO 2 physiological forcing, is projected to increase over 61%, 69%, and 78% of global terrestrial areas, respectively. Notably, we found that the contribution of plant physiological effects () to drought characteristics, including intensity, frequency, and duration, exhibits a significant and spatially extensive declining trend with rising CO 2 across most land areas. This declining trend is robustly depicted in both the multi‐model mean and individual models. Vegetation coverage plays an important role in the spatial pattern of . CO 2 physiological forcing therefore exerts greater impacts in the tropics, particularly over tropical forests. Our results demonstrate that drought characteristics are expected to become less dependent on plant physiological effects with increasing CO 2 , a consideration essential for accurate drought projections.

The future increased frequency and intensity of heat waves (HWs) across China will exacerbate adverse effects on society and the environment. However, changes in socioeconomic exposure remain underexplored. In this study, climate model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6), together with population and gross domestic product (GDP) projections were used to investigate projected heat stress and socioeconomic exposure across China and its eight subregions under four shared socioeconomic pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) over three periods (2021-2040, 2051-2070, and 2081-2100). Our results indicate a consistent upward trend in the Universal Thermal Climate Index (UTCI) across all scenarios, with intensifying increases over time, peaking at > 6 degrees C. This suggests a continuous increase in the number of extreme heat events (EHEs) in China. Population exposure to EHEs across the four UTCI thresholds (> 26 degrees C, > 32 degrees C, > 38 degrees C, and > 46 degrees C) shows an increasing trend. Projections indicate a similar to 14-fold increase nationwide, 500-fold increase in Northwest China (NWC), and a 1000-fold in Southwest China (SWC2) under SSP5-8.5 by 2081-2100 compared with current levels. The eastern and southeastern regions, especially the Yangtze River and Pearl River Delta, show significant GDP exposure increases under SSP3-7.0 and SSP5-8.5. Population exposure is mainly driven by climatic effects under severe scenarios, whereas GDP exposure is influenced by interaction effects, particularly under SSP5-8.5 and during the 2090s. This study's findings offer actionable insights for targeted adaptation in China's diverse geographies.

Study region: Lancang-Mekong River Basin (LMRB), Brazil. Study focus: Streamflow prediction in ungauged basins is a significant challenge in hydrology. This study investigates the transferability of deep learning models for hydrological simulations in ungauged basins, focusing on how constraints like catchment attributes, meteorological forcing, and Global Hydrological Models (GHMs) improve model performance when transferring knowledge from gauged to ungauged basins. We applied the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS-BR) dataset alongside GHMs and deep learning techniques to simulate hydrological processes in the LMRB. New hydrological insights for the region: The results demonstrate that a post-processing scheme combining deep learning, meteorological data, and GHMs significantly improves model accuracy, achieving a median Nash-Sutcliffe Efficiency (NSE) of 0.64, compared to 0.50 for the baseline Long Short-Term Memory (LSTM) model without GHMs. Key factors influencing model performance include catchment attributes, climate variations, and the length of the modelling series. A notable finding is the importance of catchment attributes in defining hydrological similarity, which enhances model migration between regions with differing data availability. Cross-regional migration was particularly successful when hydrological similarities between the Amazon Basin and LMRB were evaluated, achieving an NSE of 0.86 at the Pakse hydrological station. These insights provide a novel modelling framework for hydrological simulations in data-scarce regions, emphasizing the role of physical mechanisms and hydrological similarities in improving model transferability.

Drought affects the health of natural and socio-ecological systems by altering green water evapotranspiration and blue water runoff. However, the global patterns of green and blue water responses to drought remain unclear. Here we quantified drought using root-zone soil moisture reductions and examined the resulting water deficits across different climatic and vegetation conditions. We show that drought generally reduces both evapotranspiration and runoff globally, though with pronounced spatiotemporal variability. In high-latitude humid regions, drought can initially enhance evapotranspiration, whereas runoff responds more rapidly and declines consistently throughout drought periods. Furthermore, drought reduces runoff more than evapotranspiration in humid regions, but the reverse pattern occurs in arid regions. Compared to other vegetation types, forests exhibit smaller drought-induced reductions in evapotranspiration but greater reductions in runoff. These results underscore the importance of considering background climate and vegetation when assessing drought's hydrological impacts, with important implications for ecohydrological understanding and water resource management.