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Flash droughts, characterized by their rapid onset and intensification, can evolve into long-term agricultural droughts, thereby amplifying adverse impacts on water resources, agriculture, and ecosystems. However, the propagation from short-term flash droughts to long-term agricultural droughts remains limited understood, particularly across different flash drought types. Here we developed an integrated framework that combined convergent cross mapping (CCM), the random forest model, and the copula-based Bayesian approach to investigate the propagation pathways and underlying mechanisms. We applied this framework to analyze the propagation of meteorological, soil, and evaporative flash droughts into agricultural droughts in the Middle and Lower Reaches of the Yangtze River Basin (MLRYRB) from 2000 to 2022. Our results revealed strong causal relationships between flash droughts and agricultural droughts, with an average propagation time of 36.8-48.8 days. Meteorological flash droughts showed the shortest propagation time, while evaporative flash droughts exhibited the longest. Soil flash droughts demonstrated the highest propagation frequency, rate, and sensitivity to agricultural droughts, while evaporative flash droughts showed the lowest translation rates to agricultural droughts. We further found that flash drought severity strongly influenced the propagation of all flash drought types, particularly soil flash droughts, with a threshold value of 11.2 +/- 2.3. Additionally, precipitation and vapor pressure deficit (VPD) emerged as the most critical factor for meteorological and evaporative flash drought propagation, with threshold values of 14.3 +/- 7.6 mm and 7.8 +/- 2.3 hPa, respectively. These findings can advance our understanding of flash drought dynamics and mechanisms, offering important insights for effective drought mitigation.

Global vegetation dynamics profoundly change the terrestrial water cycling processes, especially with cascading effects on hydrological drought evolution. Over the past three decades, China has experienced extensive vege-tation greening. However, it remains poorly understood how much of a role vegetation changes play in hy-drological drought. In this study, we employ a process-based distributed hydrological model integrated with vegetation dynamics, and design two scenarios using the observed Leaf Area Index (LAI) data (noted as S1) and detrended LAI data (noted as S2) to examine the effect of vegetation changes on hydrological drought across China. The results show that hydrological drought occurs frequently across China, with an average drought frequency of 31.89% and a significant drying trend (average Standardized Runoff Index (SRI) trend: 0.133 decade-1, p <0.05). Vegetation change significantly influences drought categories and drought trends, while it has a slight effect on drought frequency over the whole of China. Specifically, vegetation change accelerates the drying trend by 25.47%, with the national average drying trend declining from 0.106 decade-1 in S1 to 0.133 decade-1 in S2. Spatially, vegetation increase (or decrease) amplifies drying (or wetting) trends in 61.04% (or 21.45%) of China, particularly in semi-humid regions. These findings highlight that vegetation greening alters surface water-energy balances, thereby intensifying regional hydrological drought in most areas in China. This study provides valuable insights for drought risk assessment and water resource management in ecosystems undergoing greening.

Compound events (CEs) pose great challenges to disaster risk management as they can amplify risks to globally interconnected socio-economic systems. Identifying the hotspots of CEs and understanding their impact factors are critical for developing targeted climate adaptation strategies. Here, we analyzed 12 types of CEs, the pairwise combinations of seven different hazards (e.g, heatwave, drought and extreme precipitation) and one precondition (i.e., antecedent soil moisture), using global observations and reanalysis datasets of hydrometeorological variables across 520 major river basins during 1980-2019. The hotspots of CE were revealed based on their return periods, and the frequencies and seasonality of CEs in each continent were further explored. Lastly, the impact factors were determined among multiple atmospheric circulation variability modes using the odds ratio (OR) value. The results indicate that CE hotspots are mainly located in basin of Eastern Asia, Eastern North America, Western North America, the Mediterranean, and Northern Australia. Compound drought-heatwave events (D-H), compound antecedent soil moisture-extreme precipitation events (M-P) and spatially compound extreme precipitation events (spat.P) generally occur more frequently than other CEs, accounting for 12.97 similar to 27.05%, 5.22 similar to 22.92% and 15.68 similar to 21.13% of all compound events in the six continents, respectively. Among them, M-P and spat.P show strong seasonality only in Asia and the South-West Pacific. The El Ni & ntilde;o-Southern Oscillation (ENSO), Arctic Oscillation (AO), and North Pacific Pattern (NP) are the important impact factors of most CEs and are statistically associated with the occurrences of CEs, with 42.19 similar to 84.83%, 33.80 similar to 71.14%, and 0.79 similar to 25.35% of CEs occurring during ENSO, AO, and NP anomalies, respectively. These findings reveal the spatial heterogeneity of CEs driven mainly by atmospheric circulation anomalies, which could provide a basis for basin-scale climate risk management.

Drought indices based on probabilistic statistical distributions are widely used in drought assessment, and their calculation generally assumes stationarity in hydro-meteorological variables. However, the nonstationarity induced by climate change and human activities may largely challenge the traditional stationarity-based drought assessments. In this study, we systematically diagnose the nonstationary changes in precipitation, water deficit, runoff, and soil moisture across the Chinese mainland from 1961 to 2019. These hydro-meteorological variables are key inputs for the calculation of meteorological, hydrological, and agricultural drought indices. Additionally, we investigate the impact of nonstationarity in hydro-meteorological variables on drought assessments. We find that 44.7

Flash droughts pose severe risks to vegetation growth and ecosystem stability. Vegetation recovery time following flash drought is a key indicator of ecosystem resilience. However, global patterns and drivers of vegetation recovery across climate zones remain unclear. The Ko & uml;ppen-Geiger classification links background climate and ecological responses, providing a framework for assessing recovery. This study analyzed the spatiotemporal recovery patterns from 2001 to 2023 using gross primary productivity (GPP) and solar-induced chlorophyll fluorescence (SIF) data. The key drivers were examined across different climate zones using SHAP analysis based on the XGBoost model. Dominant factor distributions were clarified using partial correlation analysis. Results indicated: (1) The global mean vegetation recovery time was approximately 55.70 days. Tropical climates showed the shortest recovery time, while arid climates and Mediterranean subtypes exhibited longer recovery time. Recovery time increased in 52.91% of the vegetated area, while 47.09% showed shortening trends. Notably, shortening trends were more prevalent in ecosystems historically exposed to chronic climatic stress (hot-summer temperate, monsoon continental, and arid zones). (2) Precipitation and shortwave radiation were the primary drivers, jointly explaining 44.54%-56.12% of the variance in the recovery time. Temperature ranked third in most regions but contributed less than 10% in tropical climates. Soil moisture dominated in tropical and cold-arid climates, while vapor pressure deficit was critical in continental and hot-arid climates. (3) Partial correlation analysis revealed the spatial distribution of dominant factors and their influences on recovery time. In water-limited regions (arid zones and hot-summer continental climates), increased precipitation facilitated recovery, whereas heightened radiation exacerbated water stress and delayed recovery. Conversely, in tropical climates and cold-summer continental climates, vegetation recovery showed a stronger sensitivity to energy availability: higher radiation promoted recovery, whereas excessive precipitation inhibited it. These findings are critical for predicting ecosystem responses to climate change and for formulating targeted adaptation strategies.

Gridded hydroclimate reconstructions spanning past centuries are essential for understanding long-term spatiotemporal hydroclimate variability. However, discrepancies among existing datasets-arising from differences in proxy types and reconstruction methods-introduce substantial uncertainty in regional assessments. In this study, we compiled and comprehensively evaluated eight major gridded hydroclimate reconstructions for Asia using instrumental and newly developed proxy data. The intercomparison reveals distinct regional differences in reconstruction skill, reflecting variations in proxy coverage and methodology. Notably, both droughtand pluvial-affected areas have markedly expanded since the 1950 s, marking the onset of intensified hydroclimatic variability across Asia. Further analysis identifies five significant regional hotspots-two drought-prone and three pluvial-prone-where the frequency of extreme events has notably increased compared with earlier centuries, indicating a shift toward more variable hydroclimatic conditions in recent decades. These findings highlight strong spatial contrasts in hydroclimate change, providing an essential reference for paleohydroclimate research and the refinement of future gridded reconstructions in Asia.

Soil drought and atmospheric drought can have devastating impacts on ecosystems and society. In this study, by employing copula models, correlation analysis, and structural equation modeling, we reveal the occurrence characteristics and driving factors of soil-atmosphere compound drought events from 1980 to 2023. We found a significant negative correlation between soil moisture (SM) and vapor pressure deficit (VPD), and their joint distribution showed a bimodal pattern. This highlights the need to examine extreme events. In addition, the joint occurrence probability of extremely low SM and extremely high VPD was substantially higher than the expected probability under the assumption of independence. This indicates that extremely low SM and extremely high VPD do not occur independently, but usually occur as compound events. Compound extreme drought events start in mid-June on average, at day of year (DOY) 165, with a significant trend toward an earlier start. They end in late July, at DOY 206, showing a noticeable delay. Additionally, the start and end of compound extreme droughts show notable latitudinal differentiation. The frequency, duration, and exposed area of compound extreme droughts have all increased significantly, and the intensity of these events has intensified, particularly on the Loess Plateau and in the middle and lower reaches of the Yangtze River. The compound extreme drought of 2022 was the most severe in the past 44 years. The geographic centroid of these droughts shows a trend of southward migration. The contribution of compound extreme drought duration to severity exceeded the contributions of SM and VPD. These findings advance our understanding of contemporary compound extreme droughts and are critical for accurately assessing the impacts of extreme climate events under ongoing climate change.

River networks release significant amounts of carbon dioxide (CO2) into the atmosphere via gas exchanges at water-air interface, profoundly affecting the global carbon cycle. Hydrological drought, characterized by negative anomalies in river discharge, can affect the transport and decomposition of organic matter, thereby significantly impacting riverine CO2 emissions. However, the extent to which hydrological droughts affect CO2 emission fluxes remains unanswered. In this study, we developed a framework to quantify the impact of hydrological droughts on CO2 emissions from the Yangtze River networks. We found that hydrological droughts led to approximately 33 % reduction in CO2 evasions in Yangtze River networks compared with non-drought periods. Moreover, the reduction in CO2 emissions across all stream orders of rivers showed significantly positive correlations to drought severity (p < 0.001). Notably, the emission reduction primarily resulted from river width contraction, which diminished the water-air interface area and consequently limited CO2 evasion. These findings highlight the importance of deepening our understanding of the impact of hydrological droughts on riverine CO2 emissions.

Abstract Traditional static designs of urban stormwater storage systems are increasingly inadequate for addressing extreme rainfalls. Recent advances highlight optimizing both spatial layout and real‐time control (RTC) of storage facilities, yet most of these decisions are made sequentially, overlooking how layout constraints can affect RTC efficiency and attain additional benefits from spatial‐temporal coordination. This study developed a synergistic optimization framework for coordinating stormwater storage system designs with their operational intelligence. This framework integrates a predictive target flow allocation‐based global control method with the optimization of storage capacity distribution, producing combined solutions of layout designs and control settings. Our methodology was applied to an urban catchment in Shenzhen, China, to evaluate the flood control performance under design and monitored heavy storms. Results show that the global RTC method substantially reduces flood peaks and runoff volumes under critical heavy storms. Compared with fixed‐layout or sequential optimization scenarios, integrating global RTC with synergistic optimization achieves the highest performance improvement over static control, with peak flow reduction rates increased by 11%∼32%. Moreover, the synergistic optimization identifies a subset of strategically located, high‐capacity control nodes contributing more than 98% of the system‐wide benefits, thus avoiding unnecessary RTC retrofitting elsewhere. These findings demonstrate the practical value of the synergistic optimization framework in guiding adaptive and efficient urban stormwater management.

Climate warming has profoundly reshaped soil moisture (SM) dynamics and land-atmosphere feedbacks, leading to widespread SM declines and strengthened SM-temperature coupling, with serious implications for agriculture and ecological stability. Yet, the long-term evolution of this coupling remains poorly constrained, particularly in regions experiencing concurrent warming and wetting. Here, we reconstruct two millennia of early-summer SM in the northeastern Tibetan Plateau (NETP) to place recent hydroclimatic change in a long-term context, and combine this record with independent temperature reconstructions, observations, and model simulations to investigate recent changes in SM-temperature coupling. We show that recent decades are anomalously wet relative to the past two millennia, mainly due to enhanced precipitation. Against this unusually wet background, observations, reconstructions, and model simulations consistently reveal that the negative SM-temperature coupling has nevertheless strengthened since the 1960s, reaching a magnitude unprecedented over the past two centuries. Intensified extreme heat events (EHTs), driven by quasi-stationary high-pressure anomalies and amplified by ongoing anthropogenic warming, explain the coexistence of long-term wetting with stronger shortterm hot-dry coupling. By elucidating the evolving hydroclimatic variability and land-atmosphere interactions, our study highlights emerging climate risks in high-altitude Asia and provides insights for improving future hydroclimate projections.

Drought can affect carbon cycle in terrestrial ecosystems, and this effect will become more concerned as the increased frequency and intensity of droughts under global warming. Quantifying the influence of divergent biochemical and hydrometeorological variables on gross primary productivity (GPP) during drought is critical for carbon emission reduction. In this study, we develop a method combining the Noah-MP land surface model (LSM) and structural equation model to investigate how the sensitivity of GPP to multiple ecosystem variables changes during precipitation deficit related meteorological drought in mainland China, and evaluate the GPP losses caused by representative drought events. The results suggest that Noah-MP LSM can well simulate the water carbon cycle processes in mainland China, with the Pearson correlation coefficients between modeled and observed latent heat and GPP exceeding 0.9 during 2001-2022 (p < 0.05). Shortwave radiation (Rs), air temperature (Ta) and leaf area index are the dominate factors affecting GPP. The sensitivities of GPP to Rs and Ta are higher in southern China compared to northern China. Moreover, both the sensitivity of GPP to Rs and Ta is reduced during three representative drought events, with the exception of grasslands in the Yangtze River Basin in 2022, indicating that precipitation deficit related meteorological drought regulate photosynthesis efficiency through Ta and Rs. Noah-MP LSM suggests that the three representative severe drought events, occurring in northern China in 2002, Yangtze River Basin in 2013 and 2022, have resulted in 38.12 +/- 27.99, 30.61 +/- 19.70 and 90.22 +/- 40.01 Tg C loss, respectively. Our results can reflect ecosystem changes during drought and improve the understanding of drought's effects on physical and biochemical processes in ecosystems.

Explosive tropical volcanic eruptions can trigger widespread hydroclimate anomalies across Eurasia, yet the underlying dynamical pathways remain poorly understood. Here we show that large tropical eruptions consistently induce concurrent summer droughts over South Asia and northern East Asia during the past millennium, as revealed by proxy records and climate model simulations. Volcanically induced suppression of monsoon convection over South Asia reduces diabatic heating, exciting a Rossby wave response resembling the negative phase of the circumglobal teleconnection (CGT). This upper-tropospheric anomaly, robust across tropical ocean states, promotes northerly winds and strong subsidence that suppress rainfall over northern East Asia. The CGT-like teleconnection is robustly reproduced across tree-ring-based CGT reconstructions, last-millennium climate simulations, and idealized modeling experiments. Our findings identify a previously underappreciated volcanic-CGT-drought linkage, offering insights into the predictability of continental-scale climate anomalies under external forcing.

Hydrological drought occurs frequently all over the world and has a great impact on human beings. Hydrological drought attribution contributes to a better understanding of the mechanisms of drought occurrence, improves the accuracy of predictions of drought events, and can provide a basis for drought risk reduction. At present, hydrological models which possess physical mechanisms are widely used in attribution analysis. However, this kind of models is complex in calculation, and has very limited time scale. In this study, we developed a hydrological drought attribution method via AdaBoost algorithm. The method divided the study period into natural period unaffected by non-climatic factors and impacted period. Taken the natural period as training period, the impacted period as test, the runoff was obtained to calculate the three-months standardized runoff index (SRI-3). Based on the run-length theory, we calculated average drought characteristics in the impacted period. Finally, the proportion of the average drought characteristics obtained by simulated SRI-3 series to those obtained by observed SRI-3 series is considered as the contribution of the climatic factors to the drought events. We applied this method in the Yangtze River Basin and the results showed that climatic factors are the dominate factors affecting hydrological droughts in this region, with the contributions at all the gauge stations are over 50%. Among all the drought characteristics, average drought severity is the most affected by the climatic factors, the corresponding contributions are all greater than 100%, shown as “excess contributions” (with non-climatic factors shown as negative contributions). Through the applications in various sub-basins of the Yangtze River Basin, the method was shown to provide new ideas for hydrological drought attribution, and the method can also be extended for applications such as meteorological hazards attribution, stock market volatility attribution and so on.

The intensification of global change has led to frequent atmospheric and soil drought events, posing severe threats to global ecosystems. Although soil drought (characterized by soil moisture, SM) and atmospheric drought (characterized by vapor pressure deficit, VPD) often co-occur, their combined effects are rarely quantified as compound droughts. This study integrates observational data and Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations with correlation analysis, copula models, and machine learning to investigate the occurrence and impacts of compound extreme droughts. Our findings reveal that SM and VPD exhibit bimodal distributions, with synchronized extreme soil droughts and extreme atmospheric droughts occurring more frequently than expected from individual extreme events. Compared to the historical simulations (1920-1999) (-0.22 gC center dot m- 2 center dot day- 1), the impact of compound extreme droughts on gross primary productivity (GPP) are projected to be more severe in the future simulations (2021-2100) (SSP126:-0.26 gC center dot m- 2 center dot day- 1; SSP370:-0.33 gC center dot m- 2 center dot day- 1), with particularly pronounced impacts in semi-arid regions. The compound drought stress on GPP exhibits significant variations across vegetation types and along the climatic aridity gradient. With increasing carbon emission scenarios, CO2 becomes a crucial regulatory factor in compound drought stress on vegetation productivity. The negative impact intensity and spatial extent of extreme soil drought on GPP far exceed those of extreme atmospheric drought, indicating that SM will play a more critical role in extreme drought stress on vegetation productivity. These findings highlight evidence that extreme drought events weaken vegetation carbon sequestration, providing essential insights for accurately assessing the interactions between vegetation and climate in China under climate change scenarios.

Long-term streamflow records are essential for understanding changes in extreme hydrological events, especially drought and flood. Most existing long-term streamflow data are available at annual temporal resolution, with monthly records being scarce, which limits the ability to reveal the variability of extreme hydrological events at monthly time scale. In this study, we developed an adaptive weighting strategy (AWS) based on the Mass Balance Regression (MBR) framework to reconstruct historical monthly streamflow using tree-ring data. The monthly streamflow in the upper Brahmaputra River (UBR) for the last five centuries (1500-2010) is reconstructed by AWS using a multi-species tree-ring network. The AWS significantly improved the reconstruction performance in refining monthly streamflow estimates compared to the original MBR method, with the coefficient of determination (R2), reduction of error (RE), and coefficient of efficiency (CE) cumulatively increasing by 56.3%, 39.9% and 53.2%, respectively. Further investigation using the reconstructed monthly streamflow data demonstrates that recent drought and pluvial events are highly unusual within the context of the last five centuries. In particular, the frequency of drought-flood abrupt alternation events (DFAAEs) shows a fluctuating decreasing trend since the mid-19th century, while the pre-instrumental (1500-1950) DFAAEs were much more frequent and intense compared to 1950-2010. Our study offers critical context and fundamental data for hydrological risk analysis and water resources management in a perspective of centuries of historical time scale.

Evapotranspiration (ET) is an essential variable in the global water cycle. With the development of remote sensing techniques, multiple large-scale ET products based on different algorithms have been developed to accurately estimate ET. However, the performance of these products suffers from various factors, including input datasets, algorithms, and environmental factors. It is critical to analyze the accuracy, uncertainty and spatiotemporal pattern of various ET products for selecting the optimal product and understanding the ET process. In this study, we systematically compared the performance of six ET products, including ERA5-LAND, GLASS, GLDAS, GLEAM, PMLV2, and SSEBop, from 2005 to 2020 across China. The comparison was conducted at the monthly scale, utilizing eddy covariance observations from eight flux tower stations for point-scale evaluation, and employing the water balance method to derive ET in 24 basins for basin-scale assessment. The threecornered hat (TCH) method was then utilized to quantify the uncertainty of these products at basin-scale. Furthermore, we analyzed the spatiotemporal distribution of ET and its seasonal variation across China. The results revealed that all products effectively captured the ET variations across China at point and basin scales, particularly in semi-humid and semi-arid climate regions covered by forest, but with significant variability in metrics among these products. Generally, GLEAM and PMLV2 demonstrated the best correlation coefficient (r) and root mean squared deviation (RMSD), outperforming the others. The uncertainty analysis indicated that GLASS achieved the lowest uncertainty at 5.53 mm/month while SSEBop showed the highest uncertainty at 11.45 mm/month. Regarding the spatiotemporal pattern of ET, these products consistently displayed an ascending trend from northwest to southeast, with the annual ET ranging from 395.18 mm in SSEBop to 504.04 mm in ERA5-LAND. However, substantial interannual and seasonal discrepancies of ET were observed widespread throughout China. This research provides a reference for selecting and applying the suitable ET product in China to facilitate the sustainable water resource management.

Anthropogenic climate change has significantly exacerbated the frequency and severity of Compound Drought and Heatwave (CDHW) events, increasing risks such as water shortages, wildfires, and heat-related mortality. Previous studies often use a single drought index, such as the Standardized Precipitation Index (SPI) or the Standardized Precipitation Evapotranspiration Index (SPEI), while our study uses both SPI and SPEI to elucidate the effect of different drought indices on the quantification of population exposure to CDHW events. Six General Circulation Models under four future Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) are considered. Exposure is defined as the product of CDHW Magnitude (degrees C) and the population in each region (million people), providing a quantitative measure of how CDHW events affect populations. The role of potential evapotranspiration (PET) in CDHW population exposure projections is examined by comparing SPI, which considers only precipitation, with SPEI, which accounts for both precipitation and PET in drought measurements. Results show that after 2050, CDHW Magnitude population exposure diverges significantly across scenarios, with SSP3-7.0 exhibiting the largest increase, reaching 0.72 (SPI) and 1.78 (SPEI) million person-degrees C by the end of the century. Regions such as Western Africa (WAF), Southeastern Africa, and South Asia (SAS) experience the largest increases in population exposure under SSP3-7.0 with SPEI, reaching 6.93, 6.77, and 5.56 million person-degrees C, respectively. Additionally, regions like Western & Central Europe, the Mediterranean, WAF, Western Central Africa, Eastern Asia, and SAS display heightened sensitivity to PET, with discrepancies between SPEI and SPI projections exceeding 1 million person-degrees C. Attribution analysis reveals that climate change, particularly when drought is calculated using PET by SPEI, is the primary factor, followed by interaction change and population change. These findings emphasize the critical role of PET in CDHW projections and the need for region-specific adaptation strategies to manage escalating risks in highly vulnerable areas.

Study region: The study focuses on mainland China. Study focus: As climate change exacerbates drought in China, accurate drought risk assessment is crucial for formulating adaptation strategies. The study established a comprehensive machine learning (ML)-based framework for meteorological drought risk assessment, integrating 13 indicators across hazard, exposure, and vulnerability categories. We employed Artificial Neural Networks (ANN) and Random Forest (RF) to evalute historical drought risk. The best-performing model was applied to project future drought risks from 2021 to 2100 under Shared Socioeconomic Pathways (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). New hydrological insights for the region: The projected future scenarios indicate a significant increase in meteorological drought risk, particularly in western China (QH, GS, NX, SC, and some regions of XJ), with high drought risk under scenarios of higher emissions. Notably, under the SSP3-7.0 scenario, rapid population growth exacerbates exposure, leading to 14.1 % of areas presenting very high drought risk. This ML-based drought assessment framework not only identifies high-risk drought areas effectively but also provides essential insights into the implications of various emission scenarios on drought severity and frequency. These insights are critical for enhancing regional drought resilience and forumulating drought risk management strategies under climate change.

Recent increase in compound drought and heatwave (CDHW) events has resulted in serious socio-economic impacts globally as well as in China and attracted growing concern. However, the underlying uncertainty in Earth system models may reduce the confidence levels in extreme events projections. Previous studies demonstrated the effectiveness of the emergent constraint (EC) method in addressing this issue, while they primarily focused on the treatment of climate variables or single extremes. The potential of EC in reducing the uncertainty of compound extremes has not been sufficiently evaluated yet. Here, we construct the EC relationships between historical daily maximum temperature and future (2021-2100) changes of CDHW events characteristics (duration, severity, and magnitude) using 24 CMIP6 models under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). The application in mainland China presented that EC decrease the variance of CDHW events characteristics by 35 % (duration), 25 % (severity) and 21 % (magnitude) on average compared to raw projections. Specially, EC performs best under SSP3-7.0 scenario, with variance reduced by 42 %, 32 % and 29 % for duration, severity, and magnitude, respectively. By the end of 21st century, constrained growth (relative to 1981-2010) in CDHW events duration, severity, and magnitude are projected to reach 8.55 (+3.86)-31.58 (+7.46) days, 0.72 (+0.25)-2.37 (+0.57) degrees C and 1.46 (+0.56)-6.20 (+1.65) degrees C, respectively. The constrained results imply that CDHW events duration, severity and magnitude are decreased by 3.58 %-6.00 %, 2.50 %-4.88 % and 2.48 %-4.95 % than currently expected, respectively, providing valuable insights for mitigation strategies and risk assessments of compound extremes.