Abstract During July–August 2022, Pakistan (PKT) experienced catastrophic flooding while the Yangtze River Basin (YRB) endured unprecedented heatwaves. While previous studies have examined the physical teleconnections, there remains a critical gap in quantifying the role of anthropogenic forcing in shaping such trans‐regional concurrent extremes. Here, we bridge this gap by combining probabilistic and storyline attribution frameworks to assess both historical and future risks of 2022‐like events. We find that the 2022 event represents a warming‐amplified analogue of the 2010 event, driven by a westward extension of the Western Pacific Subtropical High (WPSH) and an eastward shift of the South Asian High (SAH). Moisture and heat budget diagnosis reveal that dynamically horizontal moisture transport dominated the 2022 PKT precipitation, while surface cloud‐radiative forcing drove the YRB heatwave. Using complex network analysis, we uncover intensified cross‐regional linkages under SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5 scenarios. Crucially, our bivariate probabilistic attribution indicates that anthropogenic forcing accounts for nearly 100% of the likelihood of the 2022 event. Projections show that, by 2071–2100, the probability of such events could rise by 57–326 times, relative to a baseline probability of 0.0015 in historical simulations. Further, storyline attribution demonstrates that anthropogenic thermodynamics and circulation dynamics contributed approximately 60% and 40% to the 2022 event, with nearly half of the dynamic effect attributable to anthropogenic forcing. These results offer a quantitative perspective on the rising risk of concurrent Pluvial Pakistan–Hot Yangtze events under climate change, offering valuable insights for regional climate resilience and adaptation planning.

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.

Observations have shown a significant increase in flood frequency along the U.S. Southeast Coast (USSEC) since the year 2010. This increased flooding is driven both by increases in storm frequency and rising background sea level due to increasing greenhouse gases and internal low-frequency variabilities. While background sea levels are the primary factor driving the increase in flooding events, storms are responsible for the most extreme flooding events and play a crucial role in determining the maximum flood intensity. We describe here multiyear predictability of USSEC flood frequency, where much of the predictive skill originates from predictable decadal scale variations in the Atlantic Meridional Overturning Circulation (AMOC) which impact regional sea level. Using a 1/12o regional ocean model along with AMOC predictions from the Geophysical Fluid Dynamics Laboratory (GFDL) global decadal prediction system, we generate skillful flood frequency predictions along the USSEC with significant skill three years in advance. AMOC-driven sea-level variability provides a source of multiyear predictability for increasing post-2010 flood frequency along the US Southeast Coast up to three years ahead, according to a study using a regional ocean model and decadal AMOC predictions

Study region: This study focuses on mainland China, where regions are classified into arid, sub-arid, sub-humid and humid types based on the aridity index (AI) thresholds. Study focus: This study quantified the combined effects of climate change and vegetation restoration (1982-2020) on Terrestrial Water Storage Anomaly (TWSA) using partial least squares structural equation modeling (PLS-SEM). We analyzed spatiotemporal trends and partial correlation relationships of TWSA and its related climate and vegetation variables, and evaluated direct and indirect pathways influencing TWSA across different climate zones. New hydrological insights for the region: (1) TWSA showed an overall decline of-0.267 cm/a, with notable decreases in southeastern Tibet, North China, and the Ili River Basin, whereas increases occurred in South China, the Songhua River Basin, and northern Tibet. (2) Since 2000, accelerated vegetation greening exerted heterogeneous impacts on TWSA. In arid/ sub-arid regions, initial vegetation expansion improved water retention, but exceeding local water carrying capacity ultimately led to net water loss. In humid/sub-humid regions, greening promoted water conservation, benefiting TWSA in humid regions and mitigating its decline in sub-humid regions. (3) Climate change influenced TWSA directly or indirectly through vegetation change. Precipitation was the primary positive driver, with its effect amplified by vegetation dynamics in humid/sub-humid regions but dampened in arid/sub-arid regions. Rising temperatures exerted a negative indirect effect on TWSA, with amplified effects in arid/sub-arid regions post-2000 but diminishing impacts in humid/sub-humid zones.

Anticipating Arctic Sea ice variability on multiyear timescales is critical for near-term climate prediction and risk assessment. Here we assess winter Atlantic-sector Arctic Sea ice predictability using both perfect-model framework and initialized decadal hindcasts. Combining average predictability time analysis with machine learning, we identify the dominant predictable modes and their physical drivers. Externally forced anthropogenic warming dominates predictability beyond a decade, where internal variability associated with the Atlantic Meridional Overturning Circulation (AMOC) controls shorter timescales. A mature AMOC-related mode, characterized by broad-wide negative sea ice anomalies, is predictable up to four years. In contrast, a transitional AMOC mode exhibiting a dipole sea ice pattern retains skill for approximately two years. Independent machine learning predictabilities corroborate these results, underscoring the key role of slowly evolving ocean circulation, particularly AMOC variability, in shaping multiyear Arctic Sea ice predictability and near-term climate forecasts. Distinct Arctic sea-ice modes linked to the AMOC remain predictable for two to four years, while anthropogenic warming dominates longer-term predictability, according to a study combining average predictability time analysis with machine learning and decadal hindcasts

Intensifying droughts under global climate change threaten vegetation and regional ecological security. Understanding vegetation responses to drought is essential for future ecosystem dynamics prediction and adaptive management. However, most studies relied singly on correlation analysis to examine vegetation responses to drought, which may not fully capture actual response processes. Here we integrated and compared both correlation-based and event-based approaches to comprehensively investigate the drought dynamics and vegetation responses across the Lancang-Mekong River Basin (LMRB) during 1990–2022, using the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Leaf Area Index (SLAI). Our results revealed an intensification of drought in the LMRB, and short-duration droughts, defined as events lasting no more than 3 months, accounted for 85.4%–89.3% of all drought events. Comparison between the two methods showed that the event-based approach detected shorter vegetation response times of 3.2–3.8 months, whereas the correlation-based approach estimated longer response times of 6.2–6.7 months. In addition, the average vegetation recovery time was 6.1–6.3 months. Drought duration and intensity were positively correlated with vegetation anomaly duration and intensity but negatively correlated with vegetation response time. While, vegetation recovery time was negatively correlated with drought duration but positively correlated with drought intensity. Among all vegetation types, croplands and grasslands exhibited lower resilience and weaker recovery capacity, as indicated by shorter response times and longer recovery times. Overall, this study provides quantitative insights into vegetation–drought relationships and offers a scientific basis for vegetation drought risk assessment in the LMRB.

Abstract. The ocean acts as a critical carbon sink, but its efficiency in absorbing anthropogenic CO2 varies significantly over multiyear to decadal timescales. Accurately predicting this variability is essential for anticipating atmospheric CO2 growth and establishing the unperturbed baselines necessary for verifying climate mitigation efforts, such as marine Carbon Dioxide Removal (mCDR) activities. This study introduces a fully coupled physical-biogeochemical prediction framework, which integrates the NOAA GFDL Seamless System for Prediction and EArth System Research (SPEAR) with the COBALTv3 ocean biogeochemical model. We conducted ensembles of uninitialized historical simulations, data-assimilative reconstructions, and retrospective initialized predictions of global air-sea CO2 flux, evaluating their skill against observation-constrained products for a recent 40-year period (1984–2023). The uninitialized ensemble skillfully predicts the amplitude of the observed historical increases, but struggles to resolve multiyear and decadal variability. We show that initialization significantly improves prediction skill. Globally, skill is enhanced for lead times up to two years, extending up to five years in specific higher-latitude regions. Through Average Predictability Time (APT) analysis, we isolated distinct physical drivers of the dominant predictability. We find that skillful predictions up to two years are primarily governed by the El Niño–Southern Oscillation (ENSO) and its impact on tropical upwelling. Moreover, we identified a multidecadal, potentially predictable signal linked to long-term changes in Eastern Boundary Current upwelling, as well as Southern Ocean mixed layer depth and sea surface temperature. However, verifying this long-term potential predictability remains fundamentally constrained by the sparsity of multidecadal observational records in these remote or nearshore regions. This underscores the critical need for sustained, optimized ocean observations and an improved understanding of the uncertainties associated with existing observational data.

Abstract Projection uncertainty fundamentally constrains climate adaptation, yet its evolution in compound heat and precipitation extremes (CHPE) remains poorly understood. Here we quantify CHPE uncertainty using 14 CMIP6 models and two single‐model initial‐condition large ensembles. CHPE shows a delayed transition from model‐dominated to scenario‐dominated uncertainty relative to single heat extremes, owing to persistent precipitation‐side model spread. Regions with the strongest CHPE intensification, including the Amazon, South Asia, and tropical Africa, also emerge as major uncertainty hotspots. In several regions, internal climate variability contributes more strongly to CHPE than to either constituent hazard, indicating enhanced stochasticity in compound‐event occurrence. These results demonstrate that CHPE uncertainty is itself an emerging dimension of climate risk, especially where rapid hazard intensification coincides with persistently limited predictive confidence.

Understanding vegetation responses to drought is essential for future ecosystem dynamics prediction and adaptive management under the intensified climate change. However, most studies relied singly on correlation analysis to examine vegetation responses to drought, which may not fully capture actual response processes. Here we integrated and compared both correlation-based and event-based approaches to comprehensively investigate drought dynamics and vegetation responses across the Lancang-Mekong River Basin (LMRB) during 1990–2022, using the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Leaf Area Index (SLAI). Our results revealed an intensification of drought in the LMRB by SPEI and SPI, while short-duration droughts (duration ≤ 3 months) accounted for 85.4%–89.3% of all drought events. Comparison between the two methods showed that the event-based approach detected shorter vegetation response times of 3.2–3.8 months, whereas the correlation-based approach estimated longer response times of 6.2–6.7 months, and also detected an average vegetation recovery time of 6.1–6.3 months. Based on event-based approach, drought duration and intensity were positively correlated with vegetation anomaly duration and intensity but negatively correlated with vegetation response time. In contrast, vegetation recovery time was negatively correlated with drought duration but positively correlated with drought intensity. Among all vegetation types, croplands and grasslands exhibited lower resilience and weaker recovery capacity, as indicated by their shorter response times and longer recovery times. Overall, this study provides quantitative insights into vegetation–drought relationships and offers a scientific basis for vegetation drought risk management in the LMRB.

Study region: The Three Gorges Reservoir (TGR), a large channel-type reservoir on the Yangtze River, extends 595.6 km from Cuntan to the dam and is strongly influenced by longitudinal backwater. Study focus: This study investigated how impoundment alters flood-wave propagation and travel time in the TGR, using a calibrated 1D hydrodynamic model combined with a physically based wave celerity framework under multi-scenario conditions. New hydrological insights for the region: Impoundment shifted the dominant control of flood propagation from discharge (kinematic-wave behavior pre-impoundment) to reservoir water level (dynamic-wave behavior under backwater). Near-dam wave celerity increased to similar to 19 m/s at high levels, reducing mean travel time from similar to 39 h (pre-impoundment) to similar to 15 h at 175 m. The proposed celerity formulation agreed well with model results (R-2 = 0.61), and improved significantly (R-2 = 0.90) when using model-derived pre-impoundment celerity. These findings provide a physical basis for flood travel time estimation in regulated rivers and support forecasting and reservoir operation.

Abstract Stratospheric ozone depletion strongly influences Southern Ocean climate change. Using coupled climate model simulations, we quantify the transient effect of stratospheric ozone depletion on sea surface temperature (SST) over the Southern Ocean from 1982 to 2005. We find that stratospheric ozone depletion intensifies surface zonal winds south of 46°S, which increases northward Ekman transports and promotes cold water advection, resulting in SST cooling there. In addition, meridional SST gradients are enhanced across the Southern Ocean, which, in turn, prompt colder water advection driven by climatological surface winds to exacerbate the SST cooling between 46°S and 60°S. Because of the increase in stratospheric ozone depletion from 1982 to the early 2000s, the sustained Ekman transport induced horizontal cold‐water advection—though partially compensated by changes in surface heat flux and vertical advection below the mixed layer—plays a central role in maintaining Southern Ocean SST cooling and regional Antarctic sea ice expansion.

While heatwaves (HWs) and extreme precipitation events (EPEs) pose substantial threats to socioecological systems, investigations on their teleconnection patterns and driving mechanisms remain limited. Here, we leverage a two-layer complex network to reveal the synchronous pattern of HWs and EPEs and explore the role of Rossby waves in the Northern Hemisphere during June, July and August. Our findings reveal a marked increase in the affected area and spatial homogeneity of HWs across the Northern Hemisphere, while the trends in EPEs exhibit greater spatial heterogeneity. Notably, we identify a three-phase synchronization pattern of distance distributions in the two-layer networks: HW-EPE (or EPE-HW) synchronization intensifies within halfwavelength distances (similar to 2000 km), declines at intermediate scales (2000-6000 km), and rapidly decays beyond one wavelength (similar to 6000 km). High-connectivity hubs identified by the two-layer climate network with links over 2000 km include regions such as Western North America (WNA), Eastern North America (ENA), Western Europe (WEU), West Asia (WAS), East Asia (EAS), and South Asia (SAS). Furthermore, we find that Rossby waves with wavenumbers 5-8 dominate synchronous extreme events in four hub region pairs at midlatitudes. Specifically, wave-7 governs HW-HW synchronization in WNA-WEU, wave-5 modulates lowintensity EPE-EPE events in ENA-EAS, wave-7 and wave-8 dominate high-intensity HW-EPE synchronization in WAS-EAS, and wave-6 enhances EPE-HW synchronization in EAS-WNA. These insights contribute to the predictability of spatially synchronous extreme events, providing valuable information for risk mitigation of climate extremes.

Precipitation phase transitions and snowfall dynamics critically influence water resources and energy balance, yet their future changes and responses to global warming remain insufficiently quantified. Here we investigate changes in snowfall and snow fraction (an important indicator of the precipitation phase transition), along with the response of snowfall to climate change in the Northern Hemisphere for the historical period (1979-2014) and four future scenarios (2015-2100): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. This is done using observational datasets and outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models, both at a 0.5 degrees x0.5 degrees resolution. The results show significantly decreasing trends in hemispheric mean snowfall and snow fraction across periods, though snowfall trends exhibit regional heterogeneity: declines dominate southern North America, western Eurasia, and the Tibetan Plateau, while increases occur elsewhere. Conversely, snow fraction decreases over nearly all landmasses. Path analysis indicates that the contribution rate of temperature to snowfall will increase from 0.54 (historical period) to 0.63-0.75 (future scenarios). The relative impact of temperature will increase, remaining greater than that of total precipitation, with a widening gap. Particularly, regional drivers diverge: over 92 % of grids with decreasing snowfall are mainly influenced by temperature, while more than 95 % with increasing snowfall are primarily associated with higher precipitation. This study quantifies future changes in snowfall and snow fraction, as well as the relative impacts of temperature and precipitation on snowfall, highlighting differences across regions. These findings advance understanding of hydroclimatic changes and inform adaptive water management. Plain Language Summary: Changes in precipitation phase (rain or snow, commonly represented by snow fraction) and snowfall pose challenges for water resource management. We studied how snowfall and snow fraction may change across the Northern Hemisphere using the latest climate models and observations. We find a general trend of decreasing snowfall and snow fraction in the historical period, although this is not significant in most regions. Looking to the future, projections show that snowfall and snow fraction will likely decrease even more, especially under scenarios with higher greenhouse gas emissions. However, there are some regions where snowfall might actually increase. In the future, temperature will play a greater role than precipitation in affecting snowfall change, and the relative contribution of the two factors will become more disparate. This study helps us to understand how snowfall and snow fraction will change in the future, reveals the relative impacts of temperature and precipitation in governing snowfall responses to climate warming in different regions, helping to inform adaptive water management.

Under the combined influence of climate change and human activities, heatwaves have become more frequent and intense globally. Nighttime humid heatwaves (HHWs), frequently occurring in the Yangtze River Delta (YRD), pose greater risks to human health than daytime or dry heatwaves due to reduced relief from daytime heat and difficulty in heat dissipation under hot and humid environment. However, few studies focus on nighttime humid heatwave and the impact of urbanization on it also lacks. This study analyzes the spatiotemporal evolution of nighttime HHWs and evaluate the urban-rural differences in HHWs based on data from 58 meteorological stations across the YRD. The results show that nighttime HHWs became more durable, severe and frequent during 1985-2017. During the rapid urbanization phase of the YRD (2003-2017), while urban regions suffered more severe nighttime HHWs than rural areas, the differences in HHWs between the two decreased over time, as a result of the diminishing urban heat island effect, the differences in climatic characteristic as well as changes in land use and land cover. This study provides insights to evaluate the risk of nighttime HHWs in the YRD and scientific support for urban planning and climate change mitigation.

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.

Previous studies have overlooked the nonlinear dependency of drought propagation, limiting our understanding of its mechanisms. By establishing a causality chain, this study identifies the nonlinear propagation pathways of meteorological drought to agricultural drought across different climatic zones in China from 2000 to 2018 and elucidates the driving factors contributing to the divergences in propagation characteristics among these regions. The findings indicate a linear drought propagation time (DPT) of approximately two months, occurring around 25 times on average, demonstrating peer-to-peer drought propagation overall. Temperature and surface air pressure emerge as the primary driving factors, accounting for over 50% of the observed drought propagation. The interplay between precipitation (P), soil moisture (SM), and potential evapotranspiration (PET) explains the disparities in nonlinear propagation across different regions. Increased area wetness enhances nonlinear drought propagation, while linear propagation decreases. This study offers crucial insights for improving drought management and agricultural water resource strategies.

Soil methane (CH4) emissions significantly impact climate change. However, microbial controls of CH4 in global carbon cycle gain less attention than CO2, hindering the understanding of CH4 processes. Here, stemming from a baseline model (MENDmm1) with one microbial group, we developed a microbial-explicit CH4 model by representing six microbial groups following Michaelis-Menten kinetics (MENDmm6). We compared MENDmm6 with MENDfo6 (first-order kinetics) and MENDmm5 (excluding syntrophic acetate oxidation, SAO), alongside MENDmm1. Split-sample calibration and validation were conducted using high-temporal-resolution CO2 and CH4 effluxes from two soils (Oxisol and Mollisol) under five oxygen-fluctuation treatments. MENDmm6 (mean R2 = 0.66) improved CH4 modeling by 47 % over MENDmm1 (mean R2 = 0.45), with a 15 % improvement for CO2. MENDmm6-simulated methanogenic and methanotrophic biomass closely matched observed OTU abundances (r = 0.69-0.94), except for methanotrophs in the Oxisol (r = 0.13). Furthermore, including microbial processes without explicit microbial kinetics (MENDfo6) did not improve model performance over MENDmm1. Neglecting SAO in MENDmm5 failed to explain the observed hydrogenotrophic methanogenesis dominance. Our results emphasize the significance of explicit microbial communities and kinetics in CH4 modeling. The proposed MENDmm6 model, leveraging molecular measurements of CH4-cycling microbes, will enhance predictions of management impacts on CH4 emissions, crucial for climate mitigation.

Traditional hydrological models struggle to meet the accuracy requirements for runoff simulation under climate change and anthropogenic interventions. To address this limitation, we propose ensemble learning models (ELMs) that integrate optimal process-driven and data-driven models for daily runoff simulation in two typical humid basins in China: the Xiangjiang River Basin (XJRB) and Minjiang River Basin (MJRB). Model performance is evaluated by a newly developed comprehensive index CI based on entropy weight method. Our results reveal that the Xin'anjiang model outperforms other process-driven models with NSE values of 0.795 (XJRB) and 0.765 (MJRB), while the Long Short-Term Memory model outperforms other data-driven models (NSE: 0.945 and 0.955, respectively). Furthermore, hybrid ELMs surpass all single models, reducing MAE and RMSE by 15 % and 21 % in XJRB, and improving the NSE by 0.157 in MJRB. This framework enhances simulation accuracy and operational robustness, demonstrating strong potential for flood risk mitigation.