The hydrological system in Central Asia is highly sensitive to global climate change, significantly affecting water supply and energy production. In Tajikistan, the Vakhsh River- one of the main tributaries of the Amu Darya-plays a key role in the region's hydropower and irrigation. However, research on long-term hydrological changes in its two top large basins-the Surkhob and Khingov river basins-remains limited. Therefore, this study analyzed long-term climate and hydrological changes in the Vakhsh River, including its main tributaries-the Surkhob and Khingov rivers-which are vital for the water resource management in Tajikistan and even in Central Asia. Using long-term hydrometeorological observations, the change trends of temperature (1933-2020), precipitation (1970-2020), and runoff (1940-2018) were examined to assess the impact of climate change on the regional water resources. The analysis revealed the occurrence of significant warming and a spatially uneven increase in precipitation. The temperature changes across three climatic periods (1933-1960, 1960-1990, and 1990-2020) indicated that there was a transition from baseline level to accelerated warming. The precipitation showed a 2.99 mm/a increase in the Khingov River Basin and a 2.80 mm/a increase in the Surkhob River Basin during 1970-2020. Moreover, there was a gradual shift toward wetter conditions in recent decades. Despite the relatively stable annual mean runoff, seasonal redistribution occurred, with increased runoff in spring and reduced runoff in summer, due to the compensation of glacier melting. Moreover, this study forecasted runoff change during 2019-2040 using the exponential triple smoothing (ETS) method and revealed the occurrence of alternating wet and dry phases, emphasizing the sensitivity of the Vakhsh River Basin's hydrological system to climate change and the necessity of adaptive water resource management in mountainous regions of Central Asia. Therefore, this study can provide evidence-based insights that are critical for future water resources planning, climate-resilient hydropower development, and regional adaptation strategies in climate-vulnerable basins in Central Asia.

Study region High Mountain Asia (HMA) Study focus Snow water equivalent (SWE) is critical in understanding snowpack and melt processes in mountainous regions. However, the high spatial heterogeneity of snow depth and density in these regions introduces significant uncertainty in current SWE estimations. This study proposes a statistical method based on precipitation differences (DM) between top-down and bottom-up retrieval approaches to estimate SWE dynamics across High Mountain Asia (HMA), with its accuracy assessed against existing SWE products and in situ observations. New hydrogeological insights from the region Over the HMA domain, DM exhibits low combined accuracy errors (CA; mean/median of 1.10/0.87 relative to GLDAS and 1.10/1.53 relative to ERA5) and moderate overall correlations (CC of 0.35 with ERA5 and 0.26 with GLDAS), with substantially higher agreement in high-dynamic regions (CC up to 0.8). The temporal uncertainty of DM is generally within +/- 5 days during snow accumulation periods (Delay Index; DI similar to 0-5), indicating its ability to track snow evolution and assess potential water storage for runoff forecasting. Station-based evaluations further confirm that DM reliably reproduces major accumulation and ablation events, with consistently high CC and DI values comparable to ERA5. This study provides a practical pathway for characterizing water storage and seasonal water availability in data-scarce mountain environments, improving regional hydrological assessment in HMA.

Highlights What are the main findings? What are the implications of the main findings?Highlights What are the main findings? What are the implications of the main findings?Abstract Analyzing the impervious surface area (ISA) of China's three major urban agglomerations and expansion pattern of representative cities is of great significance for formulating context-specific urban planning policies and enhancing the resilience of urban development. This research employs impervious surface expansion indicators to reveal the spatiotemporal dynamics of impervious surface area, and further applies the location centrality index (LCI) to investigate the expansion structures and patterns of five megacities. The results indicate that the ISA of China's three major urban agglomerations expanded from 22,544.10 km2 to 69,348.59 km2 from 1985 to 2024; both the expansion rate and intensity have exhibited a distinct decelerating trend. However, significant regional variation exists in the peak expansion phases: the Guangdong-Hong Kong-Macao Greater Bay Area Urban Agglomeration (GBA) peaked during the Initial Exploratory Phase (1985-1995), whereas the Yangtze River Delta Urban Agglomeration (YRD) and Beijing-Tianjin-Hebei Urban Agglomeration (BTH) reached their peaks during the High-speed Development Phase (2005-2015). At the urban scale, LCI-based analysis of urban expansion across five megacities reveals that adjacency expansion type has become the predominant form of growth, exceeding 50% in most cities. These findings enhance our understanding of evolutionary trajectories of ISA within urban agglomerations and the expansion patterns of five megacities, providing a scientific basis for sustainable urban planning and urban resilience.

Study region Inland river basins in arid northwestern China (specifically the Tarim and Heihe Basins), characterized by complex rain–snow–ice mixed recharge systems and high sensitivity to climate warming. Study focus Using monthly data (1961–2020) from six representative basins, we assess how rain, snow, and ice jointly reshape streamflow seasonality. We quantify the sensitivity of streamflow to precipitation, snowmelt, and glacial runoff to reveal the mechanisms driving seasonal redistribution in these multi-source basins. New hydrological insights for the region Global warming is reshaping hydrological regimes toward seasonality flattening. Specifically, the streamflow ratio in late spring and early summer decreases by 3.46%, while autumn and winter ratios increase by 30.55%, narrowing the seasonal difference. Accelerated warming appears to precipitate the earlier onset of spring snowmelt. Conversely, the notable rise in autumn and winter streamflow is consistent with increases in precipitation (autumn: 33.2%, winter: 32.89%) and an extended glacial ablation period. Sensitivity analysis shows that streamflow is more sensitive to snowmelt (0.41) than to precipitation (0.09). Crucially, increased snowmelt exerts a buffering effect, reducing streamflow sensitivity to precipitation. Additionally, streamflow sensitivity to glacier melt intensifies with elevation. Advancing beyond single-driver frameworks, these findings demonstrate that the buffering role of snowmelt and the elevation-dependent intensification of glacier sensitivity are key mechanisms governing how multi-source recharge systems respond to warming, providing a transferable diagnostic framework for similarly complex basins.

The VRB represents one of the most important glacierized regions in the upper Amu Darya Basin (UADB), where glacier and snow dynamics play a key role in regional water resources. This study investigates glacier changes in the VRB during 2000–2025 based on multi-source remote sensing and GIS analysis, while long-term climatic variability since 1970 is used to provide background context for regional climate conditions. The results show a significant reduction in glacier area from 4440.9 km2 in 2000 to 3955.2 km2 in 2025, corresponding to a loss of 485.7 km2 (10.94%). The glaciers are mainly distributed on northern and northeastern slopes at elevations between 4000 and 5000 m a.s.l., where climatic conditions favor their preservation. The basin also contains numerous surge-type glaciers, accounting for approximately 60% of all surge-type glaciers in the Pamir region, with advances ranging from 0.4 to 3.6 km. Climatic analysis indicates a warming trend of 0.15–0.31 °C per decade during 1970–2025, accompanied by pronounced seasonal variability in snow cover and gradual decreases in surface albedo associated with increased dust and black carbon concentrations. Glacier thinning is particularly evident in the lower glacier zones, while hydrological analysis shows that glacier and snow meltwater strongly influence river runoff. These results highlight the sensitivity of glaciers in the VRB to climatic and environmental changes and emphasize the importance of continued monitoring and adaptive water resource management in the VRB.

Study region The Vakhsh River Basin, the largest tributary of the Amu River, is located in the Pamir Mountains in Central Asia. Research focus The Vakhsh River Basin, contributing ∼24% of the Amu Darya’s discharge and over 90% of Tajikistan’s hydropower, provides a key case for studying climate-driven streamflow changes. However, due to observational constraints, the coupling among glaciers, snow, and runoff in this region remains poorly understood. Therefore, this study employs the SWAT-Glacier-SRD model under a multi-objective calibration framework to investigate hydrological responses to climate change. New hydrological insights Results indicate that the model could well simulate hydrological processes in this catchment with daily NSE being 0.81 and 0.78 during the calibration and validation periods. During 1971–2019, streamflow increased significantly (1.57 mm yr⁻¹), while its composition remained stable, dominated by snowmelt (58.2%), followed by rainfall (30.5%) and glacier melt (11.3%). At the seasonal scale, the contribution of rainfall increased in early spring. Further analysis showed that streamflow timing is highly sensitive to climate conditions, with warmer, drier conditions leading to earlier and more dispersed flow, and colder, wetter conditions producing later and more concentrated flow. Despite significant warming in this region, streamflow timing shows only a slight, insignificant advance (−0.129 d yr⁻¹), likely due to compensatory precipitation effects, but this stability may not persist under intensified warming. This study provides a reliable framework for simulating cryosphere–hydrology processes in Central Asian basins.

Water resource competition has disrupted sustainable development in the Aral Sea Basin, necessitating integrated strategies for the water-food-energy-environment nexus to address challenges from ongoing climate change, ecological restoration, growing food demand, and potential hydropower projects impacting water stability. This study developed a multi-objective optimization model to address these issues. Results showed relatively equitable water allocation, with Gini coefficients consistently below 0.29 across all scenarios. Agricultural water use ranged from 71.71 to 80.53 × 109 m3, while seasonal pumped hydropower storage reservoirs increased upstream controllable water to 42.91-58.47 × 109 m3 (35%-44%). Hydropower remained stable owing to reservoir coordination. However, to ensure ecological flows (35.38-37.78 × 109 m3), crop areas should be reduced by 14.37%-21.05% under SSP2-4.5 and 16.16%-23.93% under SSP5-8.5. A trade-off emerged between benefits and water allocation equity, particularly in high-emission, low-inflow scenarios, alongside a positive correlation between benefits and greenhouse gas emissions. These findings emphasize the critical need for integrated management of the Aral Sea Basin's interconnected resource systems.

Under global climate change, drought frequency and severity in Central Asia (CA) have risen sharply, threatening ecological security. Despite extensive studies on drought evolution, a quantitative framework for revealing the joint mechanisms and compound risks of multiple drought types remains lacking. Therefore, this study analysed droughts in CA from 1982 to 2022 by integrating multiple indicators to characterise meteorological (Standardised Precipitation Evapotranspiration Index, SPEI), agricultural (Palmer Drought Severity Index, PDSI) and hydrological droughts (i.e., Gravity Recovery and Climate Experiment [GRACE]-Drought Severity Index, GRACE-DSI). A Vine Copula model was subsequently employed to construct multidimensional dependence structures among key drought characteristics. The main findings were as follows: (1) meteorological droughts were predominantly short-term (e.g., 3 month), constituting approximately 96.7% of events, whereas hydrological and agricultural droughts exhibited substantial proportions of medium- to long-term events (e.g., larger than 6 months), at 35.5% and 68.1% respectively, indicating their stronger cumulative effects and recovery lags; (2) significant time-lagged couplings occurred among drought types, with high joint probabilities concentrated in the Tianshan Mountains and central arid core. Agricultural droughts exhibited joint probabilities above 0.8 at 3-6 month scales, while extending the timescale to 12 months substantially strengthened synchronisation across all drought categories, highlighting the importance of incorporating longer timescales in drought early warning systems; and (3) driven by increased duration, severity and intensity, the joint return periods of meteorological and hydrological droughts generally ranged between 3 and 10 months, whereas those of agricultural droughts exceeded 8 months even at short timescales. The findings can provide valuable insights into the multidimensional drought couplings in CA.

The Tienshan Mountains of Central Asia, a key region in global arid and semi-arid zones, faces highly uneven precipitation distribution due to its unique topography and climate. While extreme heavy precipitation has been widely studied, research on extreme light precipitation is limited. Additionally, spatial distribution patterns and driving mechanisms of extreme events under varying climatic and geomorphic conditions remain underexplored. This study systematically examines the spatial-temporal trends of extreme hydro-climatic events, focusing on both extreme heavy and light precipitation, to provide insights for water resource management and disaster prevention. A distinct hydrological regime shift has occurred since 2000. The frequency anomaly of extreme light precipitation events (R1p) plunged from positive to negative, indicating a marked decline, whereas extreme heavy precipitation events (R99p) surged, reflecting a substantial increase in frequency. Spatially, a prominent dipole pattern is identified around 80 degrees E, where extreme heavy precipitation frequency increases eastward and decreases westward. Vertically, the mid-altitude zone acts as an amplification center, exhibiting the sharpest intensification of heavy precipitation and the steepest decline in light precipitation frequency. These patterns result from the combined effects of Tibetan Plateau thermal dynamics and monsoon-driven moisture transport, creating distinct differences in extreme precipitation between the eastern and western Tienshan. Future studies should explore the interactions between the plateau and atmospheric circulation to improve the prediction and mitigation of extreme events, aiding water resource management and disaster preparedness.

Global warming has intensified the hydrological cycle, leading to more frequent extreme hydroclimatic events. In arid regions, this has further complicated the flood-generating mechanisms in inland river basins with multiple water sources, and has had important impacts on flood processes. Focusing on the Tarim River Basin, the largest inland river basin in China, this study develops an integrated framework that combines event identification, feature-based clustering, and machine learning attribution. The framework characterizes flood type structures, spatial patterns and key climatic drivers under changing environmental conditions. Using daily streamflow data from seven mountain outlet stations (1970-2020), flood events were objectively identified and classified based on flow characteristics. Three distinct flood types are identified, including multi-peak, long-duration floods, short-duration flash floods with rapid rise and recession, and single-peak, stable floods. Analysis showed that single-peak floods are dominant overall, accounting for 45.6%, yet pronounced spatial contrasts exist across the basin. Multi-peak floods occurred more frequently along the northern slope of the Kunlun Mountains, representing 32.0% of events, while flash floods dominate the southern slope of the Tianshan Mountains, accounting for 40.6%. These patterns reflect differences in river recharge sources and basin-scale hydrological regimes. Moreover, attribution analysis based on XGBoost-SHAP reveals that precipitation is the dominant driver across all flood types, although its interactions with other factors vary. Multi-peak floods are jointly influenced by event-precipitation and snowmelt, flash floods by intense short-term rainfall combined with antecedent soil moisture, and single-peak floods by a more balanced influence of precipitation and antecedent temperature. This study advances the understanding of flood typologies and their hydroclimatic controls in arid inland basins and provides new insights into the complex and scale-dependent mechanisms of flood generation under a warming climate.

Central Asia (CA), a region highly vulnerable to drought, is experiencing increasingly severe drought conditions under climate change. However, current understanding of the spatiotemporal dynamics of drought, its propagation mechanisms, and the quantitative contributions of key drivers, particularly snow cover and vegetation, remains limited. Therefore, a three-dimensional (3D) framework was employed to extract meteorological, hydrological, and agricultural drought (MD, HD, and AD) events and their propagation pairs, subsequently integrating the extreme gradient boosting model with the shapely additive explanations to systematically investigate the spatiotemporal evolution of growing-season drought events across CA, propagation characteristics from MD to HD (i.e., MD-HD) and MD to AD (i.e., MD-AD), and their dominant driving factors. The main findings are as follows: (1) MD, HD, and AD events exhibited similar spatiotemporal patterns, characterized by peak severity periods during 1940-1950 and 2010-2023, a southeast-northwest banded distribution of high-severity centroids, and a dominant east-west migration direction (>73 %), (2) analysis of MD-HD and MD-AD propagation revealed a spring-season concentration (>52 %), geographically distinct hotspots (i.e., MD-HD and MD-AD in the northwestern lowlands and the northern agricultural zones, respectively), and a prevalent east-west propagation direction (>67 %), and (3) MD severity exerted the strongest influence on propagation, with elevated LAI facilitating and increased snow depth/snowmelt suppressing this process, further compounded by synergistic effects among MD characteristics. This study provides a scientific basis for the early warning of drought-induced disaster chains in arid and semi-arid regions.

Crop breeding faces the challenge of balancing yield with resource efficiency and stress tolerance. The key regulatory metabolite trehalose 6-phosphate (T6P) integrates carbon status with growth and stress responses. Manipulating T6P levels through genetic modification or chemical intervention provides a powerful tool to tackle the growing challenges of crop production.

Tajikistan, a mountainous country and a vital water tower for Central Asia, is becoming increasingly vulnerable to snow drought under climate change, threatening its snow-and glacier-fed streamflow. Yet, the impacts of snow drought on the regional hydrology remain insufficiently understood. In this study, we integrated multisource data, including the Fifth Generation European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis for Land Applications (ERA5-Land) data and hydrological station data, to systematically assess the snow drought patterns and their impacts on streamflow during 1950-2023. We identified snow drought events based on precipitation and snow fraction anomalies relative to climatological means and classified them into warm snow drought, dry snow drought, and warm&dry snow drought. The results revealed that snow drought was a recurrent phenomenon, occurring in 51.70% of the years during the study period, with warm&dry snow drought accounting for 21.90% of the total events. Both the frequency and severity exhibited pronounced spatial variability, largely governed by the elevation and snowfall fraction. Specifically, the frequency of warm snow drought was negatively correlated with the snowfall fraction, decreasing on average by 0.20 per unit increase in snowfall fraction, whereas the frequency of dry snow drought was positively correlated, increasing by 0.07 per unit increase. The streamflow analysis results demonstrated that snow drought typically reduced the warm-season discharge by 5.00%-18.00% in certain rivers, thereby exacerbating the water stress during the dry season. The results of this study advance our understanding by explicitly linking the types of snow drought to hydrological responses in Central Asia's high mountains, providing a scientific basis for climate adaptation and sustainable water resource management in Tajikistan.

Drought critically affects ecosystem productivity and regional sustainability. Mountainous regions, characterized by complex topography and diverse vegetation types, are highly sensitive to climate warming and therefore serve as natural laboratories for investigating drought evolution and vegetation responses. Here, we systematically analyzed the spatiotemporal characteristics of multidimensional droughts (SPEI, SSI, and SRI) across the Tianshan Mountains. Based on the long-term NDVI series (1982-2022), vegetation-type-specific partial least squares path modeling was employed to elucidate the pathways through which drought and environmental factors influence vegetation dynamics. We found that: (1) The meteorological, soil and hydrological drought increased significantly during 1982-2022, with a significant increase around 2005. The areas affected by extreme meteorological, soil, and hydrological drought expanded by approximately 23%, 20%, and 16%, respectively. (2) Drought trends varied markedly among vegetation types, with meteorological drought intensifying across all vegetation types. Forests showed partial mitigation of soil and hydrological droughts, whereas agricultural land experienced the most severe hydrological droughts. (3) NDVI was primarily influenced by soil and hydrological droughts, with agricultural land and sparse vegetation showing the highest drought sensitivity, whereas forests exhibited the weakest response. (4) Spatial NDVI trends in forests showed limited sensitivity to drought, and those in sparse vegetation were associated only with SPEI. In contrast, NDVI trends in grasslands and agricultural land were positively associated with both SPEI and SRI trends. This study evidences the intensification of multiple droughts and reveals vegetation-specific drought responses in the arid and semi-arid mountainous regions, highlighting the need for ecosystem-specific climate risk assessments.

Global warming has exhibited pronounced regional heterogeneity and temporal asymmetry, yet the underlying drivers and consequences of seasonal imbalance in temperature change remain insufficiently understood. In particular, asymmetric seasonal warming can fundamentally alter regional energy balance, hydrological pathways, and ecohydrological processes, potentially triggering cascading impacts across natural and human systems. Central Asia, as a typical arid region highly sensitive to climate change, provides an ideal case for investigating these processes. This study presents a comprehensive analysis of seasonal temperature variations over the past half century (1960-2020) and examines their ecohydrological implications. Our results reveal a marked shift in the dominant season contributing to long-term warming. While winter warming played a leading role during the earlier period (1960-1991), its contribution to annual mean temperature increase has substantially declined in recent decades. In contrast, spring warming has intensified significantly, with its contribution rising from 8.0% to 59.2% between the two periods, surpassing winter as the primary driver of regional warming. This transition reflects a fundamental reorganization of the seasonal thermal regime in Central Asia. The enhanced spring warming has induced profound changes in cryospheric and hydrological processes. Rising spring temperatures reduce snowfall fractions and accelerate snowmelt in mountainous regions, leading to earlier and more rapid release of water resources. Consequently, hydrological pathways have been altered, with a noticeable advance in the timing of spring runoff peaks and a decline in summer runoff. These changes disrupt the natural regulation of water availability, increasing the mismatch between water supply and demand during the growing season. In addition, intensified spring warming accelerates soil moisture depletion during early growing stages, significantly weakening the buffering capacity of soil water reservoirs. This process exacerbates water stress in subsequent months and contributes to the increasing frequency and severity of summer extreme events, including droughts, heatwaves, and hot-dry wind episodes. Such compound climate extremes pose substantial risks to agricultural productivity and ecosystem stability in this water-limited region. From an ecological perspective, spring warming also reshapes the coupling between thermal and hydrological conditions. It modifies the onset, duration, and intensity of the growing season, thereby influencing vegetation phenology and ecosystem productivity. However, these potential gains in early-season growth are often offset by intensified water limitations later in the season. The resulting imbalance amplifies ecosystem vulnerability and may trigger cascading ecological risks, including vegetation degradation and reduced resilience to climate extremes. Overall, this study provides a systematic assessment of the mechanisms through which enhanced spring warming influences the ecohydrological system in Central Asia. By integrating perspectives from energy balance, hydrological processes, and ecological responses, it highlights the critical role of seasonal warming asymmetry in driving regional environmental change. The findings offer new insights into the evolution of water resources and the emergence of ecological risks under ongoing climate change, and underscore the necessity of incorporating seasonal dynamics into climate impact assessments and adaptation strategies in arid regions.

CONTEXT: Pakistan's agricultural system, ranked among the world's most water-stressed, demonstrates a critical resource utilization challenge. Despite a 21.8 % expansion in harvested area since 1991 and consuming 90 % of national freshwater resources, wheat productivity remains stagnant at half the global average. This disconnect between input use and output is further exacerbated by 50 % groundwater over-extraction, declining irrigation efficiency, and increasing reliance on chemical inputs. Collectively, these trends reveal the systemic fragility of input-driven growth and underscore the urgent need for an integrated water-energy-food (WEF) nexus approach to reconcile productivity with sustainability. OBJECTIVE: This study has three key objectives: (1) quantify dynamic relationships between five critical agricultural inputs and productivity, (2) project sustainability thresholds under current practices, and (3) develop transferable optimization frameworks for water-scarce agricultural systems. METHODS: We employ Autoregressive Distributed Lag (ARDL) cointegration analysis to examine long-term re-lationships and short-term dynamics between annual agricultural productivity (AAP) and five key inputs: agri-cultural water withdrawal (AWW), energy utilization (TEU), cultivated land area (THA), pesticide use (TPU), and fertilizer use (TFU) over a 30-year peroids (1991-2021). Additionally, Autoregressive Integrated Moving Average (ARIMA) forecasting models were employed to project future scenarios (2022-2031) for both inputs and AAP. The approach validates cointegration through rigorous diagnostic testing (ADF/PP, CUSUM), ensuring robust model performance for forecasting productivity (AAP) under varying input scenarios. RESULTS AND CONCLUSIONS: The findings reveal unsustainable input trajectories: a projected 15.1 % increase in productivity by 2031 would require continued expansion of land (+21.8 % compared with 1991), pesticide use (+82.25 %) and fertilizer application (+19 %). Meanwhile agricultural water (-4.22 %) and energy avail-ability (- 6.15 %) are declining, highlighting that these critical resources are becoming increasingly limited. This combination of rising input demands and decreasing essential resources highlights the urgent need for policy interventions such as precision irrigation, integrated nutrient management, and pesticide regulation to avoid ecological collapse. SIGNIFICANCE: This research provides the first quantitative framework demonstrating the infeasibility of area-expansion strategies in Pakistan's agriculture. The findings call for immediate policy shifts toward precision irrigation, renewable energy integration, regulated agrochemical use and strengthened institutional coordination across water, energy, and agricultural sectors. The proposed WEF nexus framework offers scalable, evidence-based solutions for improving resource efficiency and food security in Pakistan and other semi-arid regions globally.

The arid Central Asia, one of the world’s largest non-zonal drylands, is highly vulnerable to global climate change due to its fragile water–ecosystem. Recent warming in this region far exceeds the global average, driving profound hydrological and ecological shifts. This study analyzes water cycle changes and ecological risks to reveal how warming is reshaping regional dynamics. Results show declining solid precipitation and an increasing frequency of compound drought–hot events. Extreme droughts have intensified in recent decades. At the same time, clear signs of cryospheric degradation are evident, including widespread glacier retreat, declining glacier coverage, and a shortening of snow cover duration. These changes disrupt seasonal runoff, expand glacial lakes, and elevate flood hazards. Intensified evaporation accelerates soil desiccation, while aridification is spreading into adjacent zones. Overall, climate-driven water cycle reorganization is amplifying ecological risks, and posing a growing threat to regional sustainability.

Under global warming, the frequent occurrence of extreme temperature events (ETE) poses a serious threat to ecological security and sustainable socio-economic development. Understanding the spatial and temporal variation of extreme temperatures and their driving factors across multiple climate regions is a core scientific question in climate research. Using ERA5 reanalysis data and 88 atmospheric circulation indices, this study applies the Köppen-Geiger climate classification system to examine the evolution of ETE across 13 global land climate zones from 1951 to 2024 and assesses their relationship with atmospheric circulation through a multi-method framework combining trend analysis, correlation testing, and the Geodetector model. The results showed: (1) extreme warm events increased in most regions, whereas extreme cold events declined. (2) Spatial heterogeneity in ETE was evident. The cold spell duration index (CSDI) increased across large areas of Eurasia. The decline rates of frost days (FD) and icing days (ID) in high-latitude climate zones exceeded those in other regions. The increasing rates of extreme temperature value indices (TNn, TNx, TXn, and TXx) were higher in mid-high-latitude climate zones than those in low-latitude region. (3) The Northern Hemisphere Subtropical High Area Index (NHSHA), North American Polar Vortex Area Index (NAPVA), Antarctic Oscillation Index (AAO), and Asian Polar Vortex Intensity Index (APVI) exerted significant effects on global extreme temperature changes. (4) Interactions among atmospheric circulation factors produced nonlinear enhancement or two-factor enhancement effects on ETE. The explanatory power of paired atmospheric circulation variables (q=0.26–0.84) was 3