Increased atmospheric CO2 affects climate through radiative and physiological forcing, but their specific roles in the transient changes in future potential evapotranspiration (PET) and dryness/wetness remain unclear. Using simulations from seven Earth System Models of the Coupled Model Intercomparison Project Phase 6, we quantify the contributions of radiative forcing, physiological forcing, their interaction, and the direct CO2 physiological effect on surface resistance to projected changes in annual PET and dryness/wetness relative to a historical baseline. Annual PET is projected to increase globally over land, affecting approximately 98 +/- 6% of land. Global land shows an overall drying tendency, with wetting and drying projected over 41 +/- 9% and 59 +/- 9%, respectively. The increases in PET and the drying trend are primarily attributed to radiative forcing through surface warming. Spatially, radiative forcing dominates PET increases across 98 +/- 6% of land by modulating temperature and net radiation. For dryness/wetness changes, radiative forcing, physiological forcing, their interaction, and the direct CO2 physiological effect dominate over 73 +/- 5%, 4 +/- 3%, 3 +/- 3%, and 20 +/- 6% of land, respectively. Radiative forcing exerts its dominant influence mainly through its effects on precipitation (22 +/- 4% of land) and temperature (51 +/- 6%). In regions where physiological forcing or its interaction with radiative forcing prevails, dryness/wetness changes are primarily linked to associated precipitation variations. Overall, this study clarifies how radiative forcing, physiological forcing, their interaction, and the direct CO2 physiological effect shape the transient changes in future PET and dryness/wetness.

The main flood season streamflow regime in the Upper Yangtze River Basin (UYR) has been significantly impacted by climate change, posing a substantial threat to hydropower generation and electricity supply security. However, the driving mechanisms of streamflow change in the UYR remain insufficiently understood. Using reanalysis datasets and numerical experiments with CAM5.3.1, this study applies interannual increment analysis and statistical methods to analyze characteristics and sea surface temperature (SST)-related mechanisms of streamflow at Yichang Hydrological Station (1980-2023) during August, which gauges total outflow from the UYR. Results indicate that May Indian Ocean (IO) warm SST serves as a predictor and exerts Indian Ocean Capacitor Effect, triggering Kelvin waves that reinforce western North Pacific anomalous anticyclone (WNPAC). Enhanced UYR precipitation arising from the convergence of warm-humid air (from the northwestern flank of WNPAC) and cold-dry airflow (from the southwestern flank of Northeast Asia anomalous cyclone) serves as the mediator connecting SST and increased streamflow. Further investigation into the monthly-scale evolution of SST reveals that May IO warm SST transitions into August Maritime Continent (MC) warm SST through oceanic dynamics and wind-evaporation-SST feedback, thereby sustaining a cross-seasonal climatic influence on UYR streamflow. Observational data based on precipitation-streamflow relationship indicate that a 1 degrees C interannual increment in May IO (August MC) SST corresponds to an increase in streamflow of approximately 1.48 (1.26) & times; 104 m3 center dot s-1. Sensitivity experiments on May IO (August MC) SST successfully reproduce this linkage, showing streamflow increments of approximately 9.02 (8.13) & times; 103 m3 center dot s-1. These findings establish an important basis for forecasting future streamflow variability and associated hydropower output.

The daytime-nighttime compound heat wave (CHW) exerts substantial impacts on human health due to its accumulative influence persisting all day, which draws increasing attention under current global warming (GW). The impact of anthropogenic activities on CHWs has been emphasized. Yet, how and to what extent internal climate variability influences their variation remains unclear. Here, we find the CHWs over the Yangtze River valley (YRV) show an oscillatory upward trend from 1961 to 2021, featuring a marked weakening during 1981-1999 and a pronounced intensification afterwards. This decadal fluctuation and upward trend are jointly modulated by GW and Pacific/Atlantic decadal variability. A quantified analysis shows that the GW contributes 36.9% while the decadal modes jointly contribute 63.1% to the total CHW changes. More interesting, this oscillatory increase in CHW is closely related to the distinct changes in seasonal-mean maximum and minimum temperatures. The daytime maximum temperature in the YRV primarily exhibits a warm-cold-warm decadal variation, notably induced by the Pacific Decadal Oscillation, while the nighttime minimum temperature is dominated by an increasing trend due to the GW and the subsequent influence of the Atlantic Multidecadal Oscillation. The large ensemble simulation also verifies the synergistic effects of external forcing and internal variability on the CHW increase. Our study sheds lights on CHW changes and its decadal prediction.

Vegetation changes in urban and suburban zones significantly influenced surface urban heat island intensity (SUHII) through their cooling effects. However, traditional SUHII studies often relied on an oversimplified urbansuburban binary framework and failed to adequately reveal the distinct patterns of differences in vegetation changes and their thermal environmental responses between old and new urban areas. Based on Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2000 to 2020, the Normalized Difference Vegetation Index (NDVI) was used to quantify vegetation change trends in 112 major cities in China, and the cooling effects were further calculated to assess the contribution of vegetation changes in different regions to SUHII. Our findings revealed that: (1) vegetation change rates showed an overall greening-browning-greening pattern in old, new, and suburban areas; (2) citywide SUHII increased by 0.07 degrees C/year but exhibited clear regional divergence, with old urban areas declining at a rate of 0.01 degrees C/year and new urban areas increasing at a rate of 0.06 degrees C/year; (3) vegetation changes in old, new, and suburban areas contributed -38.45%, 37.67%, and 23.88% to SUHII enhancement, respectively. This study comprehensively reveals the mechanisms by which zonal vegetation dynamics influence citywide SUHII, providing a scientific basis for targeted heat mitigation strategies.

Choosing effective models for accurately estimating potential evapotranspiration (PET) is essential in various fields, including climatology, ecology, hydrology, and agronomy. However, many currently dominant PET products rely on multiple models with default parameters, which can introduce uncertainties into PET-related research. To address this issue, we derived the parameters for five widely used PET models using observations from 124 eddy covariance sites worldwide. By comparing their performances across various biomes, we identified the calibrated Priestley-Taylor and Milly-Dunne models as the optimal choices, capable of being applied effectively beyond their original observation sites. Using these optimal models, along with four widely-used meteorological datasets and annual land use and land cover data, we generated a monthly PET dataset at a 0.1° resolution during 1992-2022 across global vegetation zones. Finally, we compared the new dataset with the Global Land Evaporation Amsterdam Model V4.2a PET, including daily comparisons for each biome and annual trend comparisons during 1992-2022. This new PET dataset serves as an alternative resource for conducting PET-related research.

The Budyko hypothesis is commonly used for attributing changes in evapotranspiration (ET) and runoff (R), but it has limitations in distinguishing the specific impacts of vegetation-such as leaf area index (LAI) and the physiological effects of CO2-and land use/cover change (LUCC) on ET and R. This study aims to fill this gap by combining the Budyko hypothesis with the Shuttleworth-Wallace (SW) potential evapotranspiration (PET) model, which allows for the explicit incorporation of vegetation and LUCC. Using this new framework, we quantified the contributions of climate, vegetation, LUCC, and other land properties to changes in ET and R during the 2000s and 2010s, relative to the period from 1991 to 2000, across 732 catchments in the continental U.S. The results indicate that ET increased in over half of the catchments during both decades, while R decreased in 69 % of the catchments in the 2000s and in 46 % in the 2010s. Attribution analysis revealed that precipitation (P) was the dominant factor influencing changes in ET in over 45 % of the catchments during both periods. NonPCV (non-precipitation climatic variables) affected more than 15 % of the catchments, while the Budyko parameter n was the dominant factor in approximately 35 % of them. Regarding changes in R, P, non-PCV, and parameter n dominated in about 60 %, 10 %, and 30 % of the catchments, respectively. From the 2000s to the 2010s, around 45 % of the catchments experienced shifts in the dominant factor affecting ET or R changes, with more than 20 % transitioning primarily between P and parameter n. A comparison of the attribution of changes in ET or R, using the Budyko-type equation combined with the SW model and the Food and Agriculture Organization of the United Nations-56 Penman-Monteith model, revealed over 7 % of the catchments with different dominant factors. This indicates that overlooking the effects of CO2, LAI, and LUCC can significantly influence the attribution of changes in ET or R. This integrated framework offers a valuable reference for other regions seeking to comprehensively attribute changes in ET and R by incorporating the effects of vegetation and LUCC within the Budyko hypothesis.

Previous studies have established how regional climate variability regulates local terrestrial gross primary productivity (GPP), yet the hemispheric-scale spatial organization of GPP, coordinated by large-scale atmospheric circulation, remains poorly understood. Here, using multi-source observations and numerical simulations, we show that anthropogenic shifts in Northern Hemisphere westerlies fundamentally reorganize terrestrial GPP patterns. Around 2000, westerly curvature reversed from a southward to a northward bend over eastern Europe, Northeast Asia, and western North America, while exhibiting opposite changes over central Asia and central North America. Spatial patterns of GPP trends during 1982-2018 closely match GPP responses to westerly curvature variations. Sensitivity analyses using CESM1 large-ensemble simulations and single-forcing experiments identify greenhouse gas forcing as the dominant driver of these changes, thereby reshaping GPP through surface climatic factors. Under the RCP8.5 scenario, continued curvature changes are projected to enhance GPP growth across northern Europe, Northeast Asia, and western North America, while suppressing productivity in southern Europe and central North America. These results reveal anthropogenic forcing influences terrestrial carbon uptake via large-scale atmospheric circulation, with important implications for predicting future carbon-climate feedback.

The Asian-Pacific Oscillation (APO), a seesaw teleconnection in upper-tropospheric temperatures over the Asian-North Pacific sector, significantly influences the Northern Hemispheric climate. Thus, it is important to explore factors affecting the APO variability and associated mechanisms. Using the reanalysis data from 1980 to 2020, this study reveals that the interannual variability of spring APO is significantly correlated to the preceding autumn sea ice concentration over the Chukchi-Beaufort Seas (CBSIC), with a correlation coefficient of-0.44. Namely, a reduction of autumn CBSIC is followed by a strengthened spring APO, and vice versa. This relationship is suggested to be linked by the North Pacific sea surface temperatures (SSTs). The reduced CBSIC in autumn can induce a low-level dipole pattern with an anomalous cyclonic circulation over the Bering Sea and an anomalous anticyclonic circulation to its south, conducive to SST cooling (warming) in the central-eastern (northwestern) Pacific. These SST anomalies persist and evolve into a horseshoe-shaped pattern in spring. The enhanced mid-latitude meridional SST gradient favors a northward shift of the subtropical jet stream, increasing (decreasing) the upper-tropospheric geopotential height over the mid-low latitudes of Asia (central-eastern Pacific), which aligns with the atmospheric situation of positive APO. Meanwhile, cold central-eastern Pacific SSTs induce anomalous subsidence, which links to anomalous ascent over Asia via a zonal vertical circulation. This configuration reduces (enhances) atmospheric heating over the central-eastern Pacific (Asia), resulting in a strengthened APO. This physical process derived from the reanalysis is visible in the ensemble simulations of Australian Community Climate and Earth System Simulator Earth System Model version 1.5 (ACCESS-ESM1.5) that can reproduce the significant inverse relationship between autumn CBSIC and spring APO.

In recent decades, the lower Yangtze River Basin has experienced a notable rise in both the frequency and intensity of extreme precipitation events (EPEs). In this study, we employ a convolutional neural network (CNN) to identify circulation patterns associated with EPEs, referred to as extreme precipitation circulation patterns (EPCPs). The CNN effectively captures key atmospheric features, including a southward-shifted upper-level westerly jet stream, a deepened East Asian trough, and a southwestward extension of the western North Pacific subtropical high (WNPSH). These circulation changes promote increased water vapor and vertical ascent in the region, providing favorable conditions for EPEs. Over recent decades, EPCP occurrence has increased, and precipitation on EPCP days has intensified. These trends pertain to frequency and EPCP-day moisture transport. Moisture budget analysis reveals that the intensification of EPEs on EPCP days is primarily driven by strengthening horizontal dynamical moisture advection on EPCP days, which is closely linked to the intensification of the WNPSH and a southward weakening of the upper-level westerly jet. Our findings highlight the critical role of dynamically driven moisture transport in enhancing EPEs over the lower Yangtze River Basin in a warming climate.

A solely human-centered approach to drought assessment is inadequate. Monitoring drought from an ecological perspective is essential for developing scientifically grounded response measures, particularly in drought-prone countries such as Australia. However, the Vegetation Health Index (VHI) may be limited in accurately representing vegetation conditions because of shortcomings in the Normalized Difference Vegetation Index (NDVI). To address this, we developed a Vegetation Optical Depth (VOD)-based VHI (VHIVOD) by integrating long-term harmonized VOD data sets into the conventional VHI framework. Vegetated Australia was used as a case study to evaluate the performance of VHIVOD during 1990-2024. Based on Spearman correlations with Gross Primary Production and soil moisture, VHIVOD outperformed the NDVI-based VHI across over 70% of vegetated Australia at both national and biome levels. Although no significant long-term trend in annual VHIVOD was detected, a significant breakpoint occurred in 2002. During 1990-2001, annual VHIVOD increased across more than 78% of vegetated Australia, with 44% showing significant increases, whereas only 19% showed significant increases during 2002-2024. Regarding vegetation drought indicators, including severity, intensity, duration, and area percentage, both vegetated Australia and most biomes experienced weakening drought conditions during both sub-periods. Over 70% of vegetated areas experienced declines in severity, intensity, and duration, with significant weakening observed in over 39% of vegetated Australia during 1990-2001 and over 20% during 2002-2024. These findings highlighted the improved capability of VHIVOD in representing vegetation conditions and demonstrated its potential for vegetation drought monitoring under changing hydroclimatic conditions.

Significant winter [December-February (DJF)] precipitation over southern China (SC) is one of the key features of the East Asian winter monsoon, accounting for nearly 20% of annual precipitation in the area. While oceanic drivers of its interannual variability are extensively studied, the influence of atmospheric rivers (ARs), contributing approximately 30%-40% of the climatological wintertime precipitation in SC, remains unclear. Additionally, how seasonal forecast models capture the impact of tropical sea surface temperature (SST) variations on winter precipitation through ARs requires further investigation using objective metrics. This study identifies a tropical SST pattern involving El Ni & ntilde;o-Southern Oscillation (ENSO), the Indian Ocean dipole, and the SST anomalies over the western North Pacific (WNP), whose coevolving structure rapidly develops from the preceding summer to winter. This anomalous SST configuration generates a hemispheric-scale circulation pattern from the tropics to the subtropics, which enhances vertical wind shear and meridional moisture transport over SC, favoring increased AR intrusion into the region. Consequently, significant precipitation anomalies occur particularly near SC along 20 degrees-30 degrees N, explaining over 50% of the interannual DJF precipitation variability. These ENSO-driven precipitation changes, mediated by AR activity, are reasonably predicted by two operational seasonal forecast systems, suggesting that ENSO and its interaction with WNP SST anomalies serve as the primary sources of forecast skill for winter ARs and SC precipitation. Furthermore, a screening scheme based on the observed SST and circulation states during October and November preceding the target winter is developed to determine the years in which the dynamical model forecast skill for SC DJF precipitation is higher than in other winters.

Tornado has occurred frequently in the United States (US), with its seasonality and interannual variability tremendously discussed in previous studies. However, its long-term variabilities, including the interdecadal/ multidecadal variabilities and the linear trend, have less been comprehensively discussed and quantified. In this study, we find that the tornado occurrence in the US shows salient interdecadal/decadal variabilities, in additional to the interannual variability. A modest positive linear trend is also present, qualitatively consistent with the thermodynamic influence of global oceanic warming (GOW) on convective available potential energy and low-level southerly winds. Two internal modes, namely the Interdecadal Pacific Oscillation (IPO) and Atlantic Multidecadal Oscillation (AMO), influence the interdecadal variabilities of tornado occurrence. The IPO stimulates a stationary wave train, propagating from the Pacific to North America, resembles the Pacific-North America pattern yet on the decadal time scale, and affects local circulations and hence tornado occurrence. The AMO induces a local dipole of circulation anomalies, resembling the North Atlantic Oscillation pattern, which triggers a northwest-southeast dipole change of tornado occurrence with the northwest component stronger. Furthermore, we quantitatively estimate the relative importance of IPO, AMO and GOW on the tornado occurrence from 1950 to 2021 based on a multi-regression model. The result shows that the IPO and AMO jointly contribute about 32.6 % (19.9 %) of the decadal (total) variance while the GOW contribution is secondary.

Meteorological conditions exert substantial impacts on wintertime PM2.5 variability across China, yet the quantitative influence of distinct synoptic weather patterns remains poorly constrained. Here, we combine a machine-learning-based weather-normalization framework with weather pattern classification to quantitatively isolate meteorological contributions to wintertime PM2.5 from 2000 to 2023. Based on self-organizing maps, we identify five circulation types (T1-T5), among which T1 and T2 emerge as pollution-favorable types, increasing daily PM2.5 by more than 20-35% over northern China (T1) and by 10-20% across southern and eastern China (T2). By contrast, T3-T5 are clean-favorable. T3 reduces daily PM2.5 by up to 25% in northern China, T4 induces widespread cleansing of 10-25% reductions across central, eastern, and southern China, and T5 enhances ventilation mainly over northern China with 5-15% reductions. Interannual variations in the annual wintertime PM2.5 attributable to meteorology closely track shifts in the frequency and intensity of these patterns, with increasingly pronounced effects on severe-pollution and exceptionally clean events in the North China Plain and Fenwei Plain. Furthermore, CMIP6 projections indicate divergent future responses: under SSP1-2.6 scenario, the pollution-favorable T2 becomes less frequent while ventilation-favorable T4-T5 increase, whereas SSP5-8.5 scenario displays the opposite response, indicating a transition toward more stagnant winter circulation and elevated pollution risks. These findings provide a nationwide circulation-based quantitative framework for diagnosing meteorological drivers of PM2.5 and for anticipating air-quality risks in a warming climate.

Tropical North Atlantic (TNA) warming typically favors tropical cyclone (TC) genesis over the North Atlantic but suppresses TC formation over the Northwest Pacific during boreal summer. The TNA anomaly patterns can be classified into an eastern coastal and a western warm-pool type, but their respective impacts remain unclear. Here, we find a pronounced difference in the impact between the two TNA flavors. The warm-pool TNA warming suppresses Northwest Pacific TC genesis through a remote dynamical control, while the coastal warming promotes North Atlantic TC genesis via a local thermodynamic control. High-resolution modeling reveals that, compared with the canonical TNA warming, the warm-pool TNA warming suppresses Northwest Pacific TC genesis by 65.2%, while the coastal warming enhances North Atlantic TC genesis by 60.1%. Under greenhouse warming, increased coastal TNA warming is projected to intensify North Atlantic TC activity. Therefore, distinguishing TNA flavors is critical for improving seasonal prediction and future projections of cross-basin TC activity.

The Yangtze River Basin (YRB), home to millions of people and vital farmlands, faces increasing flood risks due to extreme summer rainfall. This study demonstrated that the western North Pacific warm pool (WNPWP) can exert significant impacts on extreme precipitation in YRB through modulating atmospheric intraseasonal activities over the northwestern Pacific, which operates on 25-80-day timescales. Extreme precipitation occurs preferentially during warm WNPWP summers compared to cold WNPWP summers owing to that intraseasonal oscillation (ISO) over the northwestern Pacific is more likely to induce extreme rainfall over the YRB during warm WNPWP summers than cold WNPWP summers. The underlying mechanism involves enhanced southwesterly moisture transport by the anomalous anticyclone over the northwestern Pacific driven by the ISO in its specific phases. Crucially, this transport intensifies remarkably during warm WNPWP summers due to the strengthened anticyclonic background manifested as the westward extension of the Western Pacific Subtropical High (WPSH) in response to warm WNPWP conditions, which is statistically tied to preceding El Nino-Southern Oscillation (ENSO) events.

Under global warming, compound hot-dry events (CHDEs) pose increasing threats across China. This study investigates the spatiotemporal characteristics of summer CHDEs and their monthly atmospheric drivers using two independent datasets (CN05.1 and satellite-based) during 1979-2022. A daily-scale Compound Hot-Dry Index (CHDI) is developed to identify co-occurring high-temperature and low-precipitation extremes. Results show that the climatological frequency of CHDEs is highest in Northwest China, while the most pronounced increasing trends are found in Southwest China. Rotated empirical orthogonal function analysis of monthly CHDI fields reveals a coherent monthly shift in the dominant mode of variability: the primary centre of action migrates from Northwest China in June to Northern China in July, and finally to the Yangtze River Valley in August. Dynamical diagnosis indicates this progression is governed by distinct teleconnection patterns. The June pattern is driven by a Rossby wave train associated with the silk road pattern (SRP), which establishes an equivalent-barotropic Central Asian ridge, inducing subsidence and blocking moisture to favour CHDEs in the northwest. In July, the dominant mode is linked to the Polar-Eurasian (POL) pattern, sustaining a Baikal High that promotes heating over Northern China while anomalous easterlies impede Pacific moisture transport. By August, an intensified and westward-extended western Pacific subtropical high becomes the key driver, causing subsidence and disrupting the monsoon flow to trigger CHDEs in the Yangtze River Valley. These findings highlight a robust monthly progression in CHDE dynamics, offering a process-based framework for understanding compound extremes in a warming climate.

Based on ERA5 reanalysis and CN05.1 gridded observational datasets,this study investigates key meteorological drivers and cross-seasonal mechanisms influencing summer(June-July-August)run-of-river(ROR)hydropower generation in the middle reaches of the Yangtze River Basin(MYRB).Results reveal synergistic regulation by the meridional displacement of the subtropical westerly jet(SWJ)and Pakistan convective activity through East Asian atmospheric circulation.Southward(northward)SWJ shifts coupled with weakened(intensified)Pakistan convection enhance(reduce)MYRB precipitation while lowering(elevating)temperatures,thereby increasing(decreasing)ROR hydropower generation.Precursor analysis identifies spring negative(positive)sea surface temperature anomalies in the northwestern Indian Ocean may strengthen(weaken)meridional thermal gradients,forcing corresponding SWJ displacements.Concurrently,Central Asian cooling(warming)in spring modifies the Arabian Sea-Central Asia pressure gradient,suppressing(enhancing)South Asian monsoon moisture transport to Pakistan.These oceanic-terrestrial thermal coupling mechanisms demonstrate significant predictability for MYRB hydropower variability.

Pre-monsoon season (March-April-May, MAM) constitutes the primary heat season across the Indian Peninsula (0 degrees-40 degrees N, 60 degrees-100 degrees E), marked by frequent occurrences of extreme surface air temperature (SAT) events. These extreme heat conditions not only impact regional climate but may also propagate remotely to East Asia via atmospheric teleconnections. Despite their significance, the monthly evolution of SAT over the Indian Peninsula and its governing mechanisms remain inadequately quantified. This study investigates the monthly variability of pre-monsoon SAT over the Indian Peninsula during the period 1970-2024, emphasizing synergistic roles of internal atmospheric dynamics and sea surface temperature (SST) forcing. Results show that SAT anomalies in March and April exhibit a spatially coherent pattern across the the peninsula, associated with the North Atlantic Oscillation (NAO) and North Pacific Oscillation (NPO), respectively. Notably, April SAT anomalies are further modulated by SST anomalies south of Greenland through wave-mean flow interactions. In contrast, May SAT anomalies display a distinct east-west dipole pattern, characterized by opposing SAT anomalies between northwestern (22 degrees-40 degrees N, 60 degrees-82 degrees E) and eastern (10 degrees-30 degrees N, 82 degrees-100 degrees E) peninsula sectors. The May dipole arises from a "Z-shaped" SST anomaly pattern in the eastern Pacific, which initiates a Rossby wave train. During its propagation across Greenland, this wave train undergoes amplification via air-sea coupling with local SST anomalies, ultimately intensifying downstream impacts on the Indian Peninsula.

Exploring the sources of decadal predictability of East Asian winter monsoon (EAWM) and enhancing decadal prediction skill can provide a crucial scientific foundation for the medium-to-long-term national infrastructure planning and disaster risk mitigation strategies. However, to what extent the decadal EAWM can be predicted, the sources of its predictability and underlying physical mechanisms remain unclear. Based on observations and numerical experiments, the present study reveals that the decadal variability of EAWM is primarily modulated by three sea surface temperature anomalies (SSTA) factors: (1) North Atlantic SSTA (NAT): anomalous NAT warming triggers downstream Rossby wave trains that amplify the Siberian High and the Japan Sea Low anomalies, enhancing the zonal pressure gradient to modulate EAWM; (2) North Pacific SSTA (NPT): anomalous NPT cooling enhances low-pressure anomalies from the northwestern Pacific to the Aleutians, driving northerly flow along its western flank to strengthen EAWM; (3) Tropical Central-Eastern Pacific SSTA (TPT): anomalous TPT cooling enhances the Walker Circulation, intensifying the low-pressure anomalies over the Maritime Continent and the meridional pressure gradient towards East Asia, which strengthens northerly wind anomalies over East Asia and reinforces EAWM. Assuming “perfect” predict (i.e., observed value) these three factors, the physics-based empirical model yields a significant temporal correlation coefficient skill of 0.65 for EAWM at a 7–10 years lead time during 1971–2017, providing an estimation of the lower bound for potential decadal predictability of EAWM.

Abstract Temperature is a key variable in alpine regions such as the Tianshan Mountains, which affects the population and economic development in lowlands by regulating the melting of snow and glaciers. The climate change over the Tianshan Mountains may have a strong climate impact. This study develops a new downscaling scheme including global climate models and DEM to simulate a high‐resolution temperature data set (90 m) in the Tianshan Mountains from 2021 to 2050. First, at low resolution, we developed a batch gradient descent‐based nonlinear regression downscaling model to simulate the relationship between CMIP5 and CMIP6 temperature and explanatory variables. Then, we input high‐resolution explanatory variables into the model to obtain downscaled (90 m) grid temperature data sets. At 30 meteorological stations, the R2 of the observations and simulations is above 0.80 under RCP4.5 scenario and above 0.60 under SSP245 scenario. The RRMSE and MARE are below 0.53, and the slope of the linear functions of the two are close to 1. The simulation results show that Tianshan will accelerate warming in the next 30 years. Under the RCP4.5 scenario, the temperature will rise the fastest in autumn with 2°C/10a, and decrease in spring with 1.4°C/10a. Under the SSP245 scenario, the temperature will rise significantly in spring and winter, with 1°C/10a. Spatially, high mountains will warm faster than plains and basins. The insights gained in this research have the potential to inform climate forecasting efforts, providing a foundation for predicting runoff dynamics, assessing regional water resources, and estimating net primary productivity.