Snow avalanches pose a growing hazard in High Mountain Asia (HMA), yet their regional patterns are strongly governed by snow-climate regimes that are sensitive to both long-term warming and large-scale circulation variability. Using reanalysis-based meteorological and snow datasets (1980–2018), we develop a spatially continuous snow-climate zonation for HMA and classify the region into maritime (11.9%), transitional (4.1%), and continental (73.0%) snow-climate regimes. We detect a systematic shift toward warmer and wetter snow-climate characteristics, with the most pronounced changes along the southeastern HMA, where the transitional regime expands markedly. Variance decomposition further reveals non-stationary controls on zonation variability: temperature dominates temporal variability in the maritime and transitional regimes (explaining ~80% of their variance), whereas the continental regime is jointly regulated by temperature and snowfall, with a substantial contribution from the North Atlantic Oscillation (NAO) through its dynamical modulation of circulation and moisture transport. These findings provide a mechanistic, regime-aware framework for stratified avalanche-susceptibility modeling and for differentiated monitoring and risk management strategies across HMA under continued climate warming.

Surface precipitation phase transition is conducive to devastating snowstorms and avalanches yet remains a global challenge due to the scarcity of surface observations. Here, we present the Real-time Precipitation Phase-Intensity Collaborative Retrieval Network (RePPIC-Net), a hybrid AI framework that quantifies surface precipitation phase from satellite observations. By integrating real-time 3D atmospheric physics fields from the AI-driven FuXi model with operational geostationary satellite observations through a hierarchical architecture, our system enables real-time monitoring of surface precipitation phase, as opposed to at least 4-hour latency of current operational systems. Validated against ground stations in China, RePPIC-Net achieves a Critical Success Index for Phase and Detection of 0.1574 (snowfall) and 0.3147 (rainfall) for 0.1-5 mm/h precipitation, outperforming 4-hour latency operational products' respective scores of 0.1001 and 0.3064. The real-time precipitation phase discrimination capability of RePPIC-Net allows the development of a satellite-based surface precipitation phase nowcasting system, meeting the need for 1-3 hour global surface precipitation phase transition warnings. RePPIC-Net provides a replicable blueprint for AI-powered real-time weather monitoring, filling a gap in wintertime weather disaster warnings.

Urban green space (UGS) serves to mitigate ambient pollutant concentrations through adsorption and dispersion effects while providing carbon sequestration via photosynthesis, contributing synergistically to urban climate and air quality improvement. This study quantifies this synergistic role by evaluating the UGS-attributed reduction in PM2.5-related health impacts, the UGS-produced carbon sequestration, and the socioeconomic value of these co-benefits, using Chinese cities as a case study. From 2008 to 2022, UGS collectively prevented an estimated 179.34 thousand PM2.5-related premature deaths and sequestered 349.81 million tCO2e, corresponding to socioeconomic values of 694.69 and 314.94 billion CNY, respectively. Driven by urbanization and the expansion of UGS in China, UGS-attributed health benefits and carbon sequestration have shown steady growth over the past 15 years. Furthermore, this study evaluates the UGS investment-benefit ratio, environmental Gini coefficient, and offsetting rate (relative to city-level PM2.5-related health impacts and CO2 emissions), revealing inequalities in UGS-related environmental investment, benefits, and responsibilities across cities. The identification of associated socioeconomic factors indicated that urbanization progress exerted a complex influence on the capacity of UGS to deliver environmental health and climate co-benefits. As an integral component of urban green infrastructure and a product of urbanization, the UGS helps offset negative environmental impacts arising from urban development and construction to a certain extent. A comprehensive and system-wide evaluation of UGS-derived co-benefits can inform UGS spatial planning and support green urbanization, Beautiful China development, and achieving Sustainable Development Goal 11.

Cloud Droplet Size Distributions (CDSD) play a crucial role in cloud microphysics and cloud-radiationprecipitation interactions. Assuming a gamma distribution, CDSD can be parameterized by the Cloud Effective Radius (CER) and the Cloud Effective Variance (CEV), representing the characteristic droplet size and the width of the size distribution, respectively. Multi-Angular Polarimetric (MAP) measurements currently provide the richest information content for simultaneously retrieving both the CER (r(eff)) and CEV (upsilon(eff)) of liquid clouds, provided that the multi-angle observations adequately sample the cloud-bow angular region. However, the strict requirements of MAP retrieval methods on the angular distribution sampling and cloud heterogeneity have hindered the realization of high spatial-resolution global CDSD retrievals. Consequently, to guarantee sufficient sampling of the rainbow angular domain, earlier MAP inversions have largely depended on aggressive spatial aggregation, such as the retrieval at similar to 150 km spatial-resolution for POLDER. Here, we exploit MAP measurements from the Directional Polarization Camera (DPC) onboard GaoFen-5 (02) (GF-5 (02)) satellite to investigate the key factors that constrain CDSD retrievals. Leveraging pre-inversion MAP Information Content (IC) analysis and neighboring-pixel weighted contributions during inversion, we develop a dedicated high spatial-resolution CDSD retrieval method based on dynamic pixel-aggregation tailored to global DPC/GF-5 (02) observations. IC simulations indicate that CER retrieval is more robust than CEV. The resulting products are evaluated against MODIS r(eff) product to quantify retrieval performance. Consistent with POLDER-based studies, our DPC retrievals of r(eff) show a global mean bias of approximately 4.8 mu m smaller relative to the MODIS product from dual-channel approach with fixed upsilon(eff) in January 2024. Notably, the CDSD retrieval resolution improves from similar to 150 km for the POLDER-based product to 3.3 km for DPC, owing to both the higher native spatial resolution of DPC and the proposed dynamic pixel-aggregation approach. Furthermore, under sufficient rainbow-angle (135(degrees)-165(degrees)) coverage, successful retrievals over ocean and land require aggregation scales no larger than 3 x 3 native DPC pixels for 68.84% and 72.09% of pixels, respectively. This result suggests that the pixel-aggregation strategies commonly adopted in current MAP retrievals may be overly conservative. Overall, this method represents an important step toward stable, high spatial-resolution global CDSD products from satellite MAP measurements and provides valuable constraints for future cloud property retrievals and climate-related applications.

Abstract Aerosols are ubiquitous microscopic particles in the atmosphere, and their diurnal variation characteristics reflect short‐term atmospheric changes that are crucial for climate monitoring and prediction. However, satellite, ground‐based, and reanalysis systems cannot simultaneously provide observational authenticity together with full temporal–spatial continuity. Using 32 years of hourly ground‐truth Aerosol Optical Depth (AOD) data from the global AERONET network, we identify eight representative modes of AOD diurnal variability through cluster analysis. The dominant diurnal patterns are strongly influenced by land cover and aerosol type. Comparison with MERRA‐2 reanalysis shows that only 12.7% of stations exhibit consistent all‐day diurnal AOD variability with AERONET observations. These results provide new constraints for understanding global aerosol diurnal behavior and offer guidance for satellite temporal sampling strategies and the improvement of satellite‐based AOD retrievals.

Abstract. Reliable avalanche forecasting is essential for protecting mountain communities and transportation corridors, but estimating avalanche occurrence from monitored meteorological and snowpack conditions remains difficult. Operational assessments often rely primarily on meteorological thresholds, although avalanche responses depend on both type-specific triggering process and the snowpack conditions. Using meteorological, pre-event snowpack, and avalanche observations collected during the 2024 and 2025 snow seasons, we analysed 37 recorded avalanche events, together with corresponding non-avalanche periods. Separate logistic-regression models were developed for dry- and wet-snow avalanches using the intensity and duration of the preceding snowfall or snowmelt process and background snow depth. Their performance was compared with otherwise identical models excluding snow depth. Under leave-one-out cross-validation, including background snow depth increased the area under the receiver operating characteristic curve from 0.72 to 0.83 for dry-snow avalanches and from 0.85 to 0.94 for wet-snow avalanches. For wet-snow avalanches, the true-positive rate increased from 0.79 to 0.92, while the false-positive rate decreased from 0.21 to 0.12. These results demonstrate that pre-event snowpack conditions provides predictive information beyond meteorological forcing alone and improves avalanche forecasting, particularly for wet-snow avalanches.

Accurate simulation of precipitation frequency and intensity over the Tibetan Plateau (TP) is crucial for elucidating regional precipitation patterns and mitigating water-related hazards under climate change, yet remains a long-standing challenge. This study evaluates the physically-refined Tibetan Plateau Climate System Model (TPCSM) in simulating precipitation amount and frequency across different intensity categories. High-resolution simulations for summer 2019 were compared against daily precipitation observations from 112 rain-gauge stations, ERA5 reanalysis, standard WRF simulations, and three satellite products (GSMaP, IMERG, and MSWEP). Quantitative evaluations show that ERA5 exhibits a substantial overestimation of total precipitation, with a bias of 3.51 mm/d and a frequency error of 31.32 d; whereas kilometer-scale WRF simulations mitigate these biases to 1.95 mm/d and 10.2 d, respectively. The refined TPCSM further enhances performance, yielding the lowest mean bias of 0.53 mm/d and a frequency error of only ∼3 d. Specifically, the refinements of physical schemes in TPCSM remarkably improve the representation of mid-heavy precipitation (>10 mm/d), demonstrating superior fidelity in capturing the precipitation frequency-intensity relationship and achieving the highest Equitable Threat Score across all intensity categories. Sensitivity experiments identify the sub-grid statistical cloud scheme as the primary contributor to these improvements; its implementation leads to a substantial 119.66 mm reduction in cumulative moisture flux convergence, corresponding to a 64.4% decrease in mid-heavy precipitation frequency. Additionally, incorporating soil organic matter and turbulent orographic form drag helps rectify the wet bias by modulating evapotranspiration and limiting moisture transport. Notably, TPCSM also corrects the prevalent overestimation of light precipitation frequency in satellite retrievals over the TP, demonstrating predictive skill comparable to or exceeding that of mainstream satellite products. These findings underscore the potential of TPCSM as an effective tool for studying precipitation variability under future climate change, thereby supporting risk assessment and water-related hazards mitigation in data-scarce regions of the TP.

This paper discusses financial flows from 14 non-Annex II countries for developing countries' climate action. The authors examined the 13 largest economies outside Annex II, plus the largest economy in Africa: China, South Korea, India, Brazil, Saudi Arabia, Argentina, Russia, Mexico, Indonesia, Israel, United Arab Emirates, Turkiye, South Africa and Singapore, The authors estimate public climate finance across bilateral, multilateral, export credits and mobilized private finance and examine trends and takeaways for meeting the $1.3 trillion goal.

Given the vast expanses of bare soil and sparse grassland in the central and western Tibetan Plateau (CWTP), the duration of precipitation serves as a critical control on key hydrological and geomorphological responses, such as runoff generation, soil erosion, and sediment transport. This study analyzes the spatial distribution of precipitation events with different durations (short-duration, medium-duration, and long-duration) across the Tibetan Plateau (TP). To explore the differences in precipitation duration between the central-western and eastern TP, we utilize hourly precipitation data from a newly established cross-sectional rainfall observation network (53 gauges) on the CWTP and the China Meteorological Administration observation (145 gauges) on the eastern TP for comparison. The main conclusions are as follows: First, short-duration (1-3 h) precipitation events contribute more than 50% of the total rainfall in the CWTP. In contrast, short-duration events contribute less than 30% in the eastern TP, where medium- and long-duration precipitation events dominate. Second, the reanalysis data ECMWF reanalysis 5 and the high-resolution atmospheric simulation data high asia refined analysis version 2 tend to systematically underestimate (overestimate) the contribution of short-duration (long-duration) precipitation events. Specifically, they exhibit mean biases of 23% and 19% for short-duration precipitation, and 34% and 20% for long-duration precipitation, respectively. Third, the satellite remote sensing precipitation data integrated multisatellite retrievals perform well in estimating the contribution of precipitation events, with biases mostly within 30% . By comparing the original and gauge-calibrated satellite datasets, the results show that calibration with coarse temporal resolutions (daily or monthly) does not necessarily improve the identification of short-duration precipitation events. Our results not only enhance the understanding of precipitation characteristics and processes over the TP, but also provide valuable guidance for hydrological modeling and the evaluation and improvement of satellite-based precipitation data.

Study region The Tuotuo River Basin, in the interior of the Tibetan Plateau, is a headwater catchment of the Yangtze River and a high-altitude cold-region basin influenced by precipitation, glacier meltwater, and supra-permafrost water. Its pronounced wet-to-dry transition from September to October makes it ideal for investigating runoff-source shifts in permafrost headwaters. Study focus Stable isotopes and hydrochemistry were combined to identify seasonal changes in runoff sources at the basin outlet. From September to October, outlet river water became isotopically enriched and major ion concentrations generally increased, indicating a marked recharge-source adjustment. Because river-water major ions fell outside the end-member mixing envelope, δ18O and d-excess were selected as quantitative tracers to estimate contributions from precipitation, glacier meltwater, and supra-permafrost water. New hydrological insights for the region Outlet observations revealed a clear shift from multi-source recharge in September to supra-permafrost-water dominance in October. End-member mixing analysis showed that supra-permafrost water increased from 55% to 83%, whereas glacier meltwater and precipitation decreased from 25% and 21–10% and 7%, respectively. Runoff evolution during the wet-to-dry transition was therefore not a simple synchronous response to reduced precipitation and meltwater input, but was increasingly controlled by delayed release of water seasonally stored in the active layer.

Important Agricultural Heritage Systems (IAHS) represents an important model for sustainable development, which faces increasing pressure under rapid modernization. This study investigates 205 Zhejiang Important Agricultural Heritage Systems (Zhejiang-IAHS) using spatial analytical methods and the GeoDetector model to examine their spatiotemporal evolution, spatial patterns, and driving factors. The results indicate that: (1) The temporal frequency of Zhejiang-IAHS formation follows an inverted U-shaped trajectory, and the spatial gravity center oscillated between Shaoxing and Jinhua across different historical periods; (2) Zhejiang-IAHS exhibits a polycentric and uneven clustering structure, high-density clusters are predominantly located in plains and basin areas, while different Zhejiang-IAHS types display distinct geographical affinities; (3) Regarding the driving mechanisms of the present-day distribution, the GeoDetector analysis reveals that the explanatory power of social factors and economic factors is significantly stronger than that of natural conditions. Specifically, contemporary elements such as cultural infrastructures are identified as dominant forces associated with the spatial clustering and retention of these heritage systems. Interaction analysis further reveals a synergistic mechanism wherein static resource endowments are activated by dynamic socioeconomic conditions, with the interaction between transportation networks and tourism development being particularly pronounced. These findings suggest that while natural endowments provided the initial foundation for heritage formation, modern socioeconomic vitality plays a decisive role in their current identification and preservation. These findings highlight that activating static natural endowments through dynamic social factors and economic factors is key to the contemporary persistence of IAHS, offering a theoretical framework for understanding their spatiotemporal evolution.

Under global warming, snow accumulation exhibits increasing variability and more frequent extremes, giving rise to dual risks associated with extreme snowfall regimes. Excessive snowfall enhances snowpack loading and instability and, when combined with triggers such as wind redistribution, rapid warming, or intense snowfall events, substantially elevates avalanche risk. In contrast, insufficient snowfall reduces snow water storage, weakens and advances meltwater supply, and intensifies seasonal water deficits, leading to snow-drought conditions with cascading impacts on ecosystems, agriculture, and water resources. These risks are driven not only by the cumulative effects of long-term warming—which alters precipitation phase, snow-season duration, and snowpack structure—but also by short-lived strong perturbations such as warm intrusions, abrupt temperature rises, and rain-on-snow events. The coupling of cumulative climate forcing and transient disturbances governs the occurrence and evolution of avalanches and snow drought across time scales, increasing the likelihood of compound or alternating risks within the same region or snow season. Observational records indicate that around 2000, snow-related hazards underwent pronounced structural shifts, with concurrent changes in the frequency, intensity, and seasonal timing of both avalanches and snow droughts, suggesting a critical turning point in snow-hazard dynamics. Focusing on this transition, the present study integrates multi-source snow and hazard datasets to characterize pre- and post-2000 regime changes, elucidate the underlying coupled mechanisms, and inform mountain hazard mitigation and climate-resilient water-resource management.

Nitrate-nitrogen losses from terrestrial ecosystems to rivers, driven by human activities and climate change, pose significant threats to water quality in China. However, comprehensive long-term quantification of terrestrial nitrate loading across China and the mechanisms driving it remain unclear. Here, we quantified long-term trends in nitrate loading from 1979 to 2018 using Community Land Model version 5 and evaluated the responses to nitrogen fertilizer inputs and climate change using a random forest model. Our results indicated that nitrate loading increased significantly over the past four decades at a rate of 3.3 × 10-3 Tg N yr-2. This increase was primarily attributable to enhanced nitrate leaching (5.2 × 10-3 Tg N yr-2), despite a concurrent decline in runoff of nitrate (-1.9 × 10-3 Tg N yr-2). Attribution results further revealed a clear pathway divergence: fertilization showed the highest explanatory importance for nitrate runoff (60.8%), whereas precipitation and temperature had greater explanatory importance for nitrate leaching. Nitrogen fertilizer accounted for 38% of total nitrate loading. In contrast, precipitation contributed more to nitrate leaching than nitrogen fertilizer. Moreover, the relative importance of predictors varied substantially among China's seven exorheic basins. Temperature showed the highest importance in southern basins, whereas precipitation ranked highest in the Haihe Basin. The influence of nitrogen fertilizer was greatest in northern agricultural basins. Together, these findings clarify the interplay between anthropogenic nitrogen inputs and region-specific climate sensitivities and highlight the need for tailored, basin-scale nitrogen management strategies.

Understanding hydrological thresholds and runoff generation mechanisms across contrasting climatic zones remains a major challenge for improving flood prediction under climate change. However, existing studies are largely constrained to single climatic regions and rely on empirical rainfall thresholds, limiting their transferability across diverse environments. This study addresses this gap by comparatively analysing event-based hydrometeorological observations from 2006 to 2022 in two representative mountainous watersheds under contrasting climatic conditions. The results reveal two distinct types of hydrological threshold control. In arid environments (the Rujigou watershed in the Helan Mountains), runoff is governed by intensity-controlled thresholds, where high rainfall intensity exceeding infiltration capacity triggers rapid hydrological responses with short lag times. By contrast, humid environments (the Longxi River watershed in the Hengduan Mountains) exhibit storage-controlled thresholds, where runoff generation depends on the combined effects of antecedent soil moisture and cumulative rainfall, reflecting saturation-excess and subsurface flow processes. A coupled threshold between antecedent storage and event precipitation is identified: when the sum of rainfall and antecedent soil moisture reaches 111.23 mm, it marks the threshold for runoff generation; when this sum reaches 260.72 mm, it indicates the threshold at which runoff begins to increase sharply. This significantly improves the explanation of runoff variability beyond conventional rainfall-only metrics. By integrating multiple indicators, including rainfall characteristics, runoff coefficients, curve numbers and lag-time correlations, this study develops a transferable diagnostic framework for identifying dominant runoff generation mechanisms. The findings demonstrate that hydrological thresholds are climate-dependent and shaped by interactions among precipitation regimes, soil properties and vegetation. Under intensifying extreme rainfall associated with climate change, these contrasting controls are likely to amplify, highlighting the need for region-specific, process-based strategies for flood early warning. This framework provides a basis for improving flood risk assessment and adaptive management in mountainous regions worldwide, particularly in data-scarce environments.

Climate change has increased temperatures and altered snowpack properties, which in turn affect avalanche activity. The extent to which climate change affects avalanche activity and how avalanche activity responds to climate change remains poorly understood, which hinders avalanche risk assessments under future climate change. In this study, we applied tree-ring evidence from trees affected by avalanches to reproduce historical avalanche events and then combined the observed meteorological and snowpack data to reveal the response mechanism of avalanche activity to climate change in the Tianshan Mountains. The study found that snow seasons with large-scale avalanche events have increased since 1943, which is contrary to the assumption that less snow under a warming climate reduces the risk of avalanches. Snow seasons in which large-scale avalanches occur are characterised by high snow depth in November (>44.4 cm) and April (>60.9 cm), together with low December temperatures (<−1.9 °C) under heavy snowfall (≥34.4 cm). Under climate change, the study area experienced a marked increase in snow-season temperatures along with rising trends in snow depth and heavy snowfall, leading to elevated avalanche risk in the middle altitudes of the western Tianshan Mountains, where human activities are intensive. This study provides a clear understanding of avalanche risk changes in the region under climate change and helps people propose climate-change adaptation strategies for avalanche risk.

Catastrophic floods triggered by extreme monsoonal rainfall have increasingly posed challenges in flood management, particularly in lowincome nations like Pakistan. The 2022 megaflood exposed substantial gaps in understanding the interplay between rainfall, sediment dynamics, and flood amplification processes. This study investigates the causes and impacts of the event, focusing on floodwater sources, geomorphological changes, and future risks and mitigation strategies. A multifaceted approach combined with remote sensing, field observations, hydrometeorological data, and climate models was employed. In July-August 2022, the lower Hindukush, Koh-e-Suleman, and Kirther ranges experienced rainfall of 200-300 mm, up to 726% above historical averages (1991-2021). This high-intensity rainfall triggered high-magnitude discharges from approximately 1250 streams, 58 % of which were primary contributors with discharge at 4000-5000 m3/s for nearly a month, causing 80%-85% of downstream damage and expanding the flooded area to 49,711 km2. Some streams recorded 5-6 m flood levels, resulting in sedimentation deposition of 0.8-1 m in residential areas and about 2 m in the streambeds. Deposition downstream and along the Indus River reached 1.5-2 m, intensifying flood risks by reducing channel capacity and increasing water levels during flood events. Streambed uplift and sediment deposition emerged as critical factors amplifying flood magnitude, severity, and inundation, with floodwater levels rising by up to 2.4-3 m in some areas, posing severe risks for future events. These findings highlight the need to revise traditional flood risk models, which often overlook sediment dynamics and underscore the future challenges posed by ongoing sediment deposition and climate change, which are expected to exacerbate flood risks in the coming decades. The study emphasizes the importance of sediment management, river erosion control, and post-event interventions. Prioritizing flood management, enhancing early warning systems, and investing in resilient infrastructure are essential strategies to protect communities and ensure long-term safety.

River and lake sediment is a crucial and sensitive area for the interaction between nature and human activities in the Earth's spheres. CiteSpace was applied to analyze the status quo of global river and lake sediment pollution management from 1983 to 2023. New ideas and application technologies for river and lake sediment pollution control were provided by tracking research hotspots and trends. The results indicated that the number of research papers increased rapidly approximately 2,000. Four productive teams were selected whose research focused on (1) the solidification and stabilization of contaminated sediment (team of Tsang, D.C.W., 2017–2020), (2) the adsorption and interception of persistent organic pollution (team of Cornelissen, G., 2008–2017), (3) the remediation of heavy metal pollution by novel nanomaterials (team of Zeng, G., 2016–2019), and (4) the remediation of heavy metal fields by plants (team of Tack, F.M.G., 2000–2005). In addition, interdisciplinary studies in this field are rare. Polychlorinated biphenyls, cadmium, copper, and other pollutants appeared successively, and the foregoing research tracks of pollutants reflect the development of industrial technology and changes in human lifestyles. Research on plant adsorption, microbial community degradation, and chemical fixation has exceeded the description of the physical and chemical properties of sediment pollutants. Since 2015, activated carbon, ecological risk, environmental change, and management have emerged. The current research highlights two new trends, namely, green environmental protection and environmental change, in terms of management risks in the fields of river and lake sediment pollution. This study contributes to an uplink sensing scheme for lake sediment pollution management in the future.

Repetitive optical observations from satellites are crucial for monitoring earth surface dynamics over time. However, optical satellite image time series is severely affected by frequent data gaps due to clouds and shadows. While synthetic aperture radar (SAR) provides cloud-penetrating capabilities to complement missing optical data, recent advancements in time series reconstruction have shifted focus from incorporating single SAR image to exploiting SAR time series. However, current methods still struggle for challenging scenarios like highly dynamic surface, persistent data gaps, and exhibit poor resilience to inaccurate cloud masks. In this research, we approach the time series reconstruction problem from the perspective of conditional generation. We propose a multimodal diffusion framework termed RESTORE-DiT, which firstly promotes the sequence-level optical-SAR fusion through a diffusion framework. Specifically, date-matched SAR time series provide under-cloud surface dynamics to guide the denoising process of cloudy areas, and date information is embedded to account for irregular observation intervals and periodic patterns. Extensive experiments on three regions have shown the proposed method achieves state-of-the-art performance. RESTORE-DiT outperforms comparison methods by 2.87 dB in PSNR and a 27.2 % reduction in RMSE on France site. SAR and date information together increase PSNR by 2.41 dB. The reconstructed optical image time series is verified to accurately reflect the crop growth condition and support for long-term vegetation observations. In addition, RESTORE-DiT can be easily extended to other conditional reconstruction or prediction tasks for arbitrary time series image data, thus facilitating spatiotemporal analysis research. The codes will be public available at: https://github.com/SQD1/RESTORE -DiT.

Abstract. Extreme wildfires threaten health, air quality, and ecosystems. Despite extensive study of meteorological links, the feedback mechanisms by which fire weather influences the long-range smoke transport remain poorly understood. This study examines the transcontinental transport of smoke aerosols emitted by intense North American wildfires in August 2024. Our analysis reveals that pyrocumulonimbus clouds (PyroCbs) formed in extreme fires exhibit strong vertical convection, injecting large amounts of smoke aerosols into upper troposphere and lower stratosphere. These lofted aerosols exhibit enhanced hydrophilicity at high altitudes, increasing the cloud condensation nuclei by a factor of 2–3. Therefore, the water cloud droplet effective radius decreases by 1/3, and the cloud fraction increases from 0.01 to 0.64, promoting the development of optically thick, high-level clouds. PyroCb efficiently lifts aerosols, prolonging their residence time and enabling long-range transport through high-altitude winds. This process significantly affects regional and global radiation, with aerosols heating downwind Europe. Smoke aerosols produced consistent effects on radiative fluxes: they reduced longwave fluxes, while increased shortwave fluxes, resulting in net anomalies of +2.84 W m⁻² over fire sources and +3.16 W m⁻² in smoke-transported areas. Conversely, aerosols over fire-source regions caused heterogeneous radiative responses, with net cooling anomalies of -2.5 W m⁻² along the US West Coast and +5.62 W m⁻² across North America. Our findings underscore the complex interplay between wildfires, smoke aerosols, and meteorology, forming a positive feedback loop that amplifies air pollution transport and radiative perturbations across continental scales.