While heatwaves in the atmosphere and oceans are well documented, soil heatwaves under climate change remain largely unknown. This gap is critical as soil provides a host of vital ecosystem services, which are highly vulnerable to soil heat extremes. Here, we present a nationwide assessment of historical soil heatwave changes across the Mainland of China using an observational homogenized soil temperature dataset. We find that deeper soils experience less intense but longer-lasting heatwaves than shallower ones. Since 1960, soil heatwave frequency, duration, and intensity have increased in all depths, with deeper soils showing greater increases in duration and smaller increases in intensity. These trends have been accelerated recently by ∼50% (∼100%) in shallow (deep) soils during 1990–2017, relative to 1960–2017. Around 60.4% of China’s croplands are facing increasing exposure to soil heatwaves, especially in major breadbaskets such as the North China Plain. Our findings underscore the growing urgency of understanding soil heatwave behaviors and impacts.

Abstract. Near-surface atmospheric moisture is a fundamental component of the hydrological cycle and plays a key role in regulating land-atmosphere exchanges and surface energy partitioning. Reliable daily high-resolution moisture data are essential for regional climate analysis and fine-scale applications, particularly for capturing short-term variability and extreme moisture dynamics. With complex terrain and a dense population, China is highly vulnerable to extreme hydro-meteorological extremes, yet existing moisture products over China are largely constrained by coarse temporal resolution, insufficient spatial detail, and limited indicators. Here, we present HiMIC-Daily, a seamless daily 1-km-resolution near-surface atmospheric moisture dataset for China, 2003–2020. HiMIC-Daily provides a comprehensive suite of six widely used indicators that characterize atmospheric moisture from different perspectives: actual vapor pressure (AVP), dew point temperature (DPT), mixing ratio (MR), relative humidity (RH), specific humidity (SH), and vapor pressure deficit (VPD). This dataset is generated using the Light Gradient Boosting Machine (LightGBM) framework, which integrates in-situ observations from 2419 meteorological stations with multiple environmental and temporal covariates, including ERA5-Land derived near-surface temperature and DPT, AVP, land surface temperature, topography, and day of year. Validation against observations shows that HiMIC-Daily achieves robust performance across all six indicators, with R2 values ranging from 0.877 to 0.989. The strongest performance is obtained for AVP, DPT, MR, and SH, with R2 values exceeding 0.985, and error metrics remain within acceptable ranges for all indicators (e.g., mean absolute error of 0.677 hPa and a root mean square error of 0.933 hPa for AVP). Compared with two existing coarse resolution products, HiMIC-Daily provides finer spatial detail, higher accuracy, and more realistic temporal variability across different climatic regions. These capabilities support spatially explicit studies of climate variability and environmental processes. The HiMIC-Daily dataset is publicly available at https://doi.org/10.11888/Atmos.tpdc.303449.

With the rapid development of digital city modeling, urban scene understanding, and the emerging low-altitude economy, large-scale LiDAR point cloud understanding has become a critical research topic at the aerospace information technology. Conventional closed-set supervised 3D instance segmentation methods suffer from limited generalization ability and high retraining costs when dealing with open-world scenarios characterized by unknown categories, long-tailed objects, and cross-domain distribution shifts. In this paper, we propose OV-Urban3D, an open-vocabulary instance segmentation framework for large-scale point clouds based on vision-language models (VLMs). The proposed method establishes a unified perception pipeline that progressively integrates 2D semantic reasoning and 3D geometric understanding. Extensive experiments on STPLS3D demonstrate the superior performance of our method. Without relying on large-scale 3D supervision and externally registered images, OV-Urban3D effectively integrates open-vocabulary semantics, 2D boundary cues, 3D structural priors, and cross-modal alignment, providing a practical solution for large-scale point cloud instance segmentation.

Accurate real-time monitoring of the thermal environment is essential for mitigating the impacts of thermal stress on human health and ecosystems, particularly in regions with limited highresolution data, such as inland China. In this study, we utilized hourly meteorological observations to derive reference thermal indices for model training and validation. We then applied Himawari-8 satellite data, digital elevation models (DEMs), and time/geographic predictors to estimate 12 human thermal indices at a spatial resolution of 5 km across Jiangxi, China. The XGBoost model demonstrated the best overall performance, with mean testing metrics for the 12 human thermal indices of R2 = 0.987, RMSE = 0.925 degrees C, and MAE = 0.656 degrees C. The retrieval results also show a high degree of consistency with in-situ observations across both spatial and temporal domains, effectively capturing the diurnal variations and spatial heterogeneity of the urban thermal environment. Our study offers a methodological foundation for the hourly estimation of human thermal indices and supports applications such as extreme-event monitoring, urban thermal environment analysis, and heat/cold risk assessments.

Extreme sunshine duration events (ESDEs) are characterized by anomalously long sunshine and intense solar radiation. ESDEs severely threaten ecosystem functions and human health by disrupting photosynthesis and elevating skin cancer risks, but how these events occur and evolve remains largely unclear. Here, we present an investigation of the synoptic behaviours and driving mechanisms of ESDEs by taking eastern China as an example, using a homogenized daily dataset. We identify four ESDE hotspots featured by persistent anticyclonic and high-pressure anomalies, which promote clear, dry, and stable atmospheric conditions. In the Middle Yangtze River valley, ESDEs are associated with a northward extension of the high-pressure system, which strengthens subsidence and suppresses moisture. In the hotspots of Central and North China, a midlatitude wave train linked to a blocking extension of high-pressure ridge from the northwest parts and a high-pressure centre over the Sea of Japan is identified. Meanwhile, Central China is also modulated by persistent near-surface anticyclonic conditions before and during the ESDEs, creating hotter and drier atmospheric columns. In comparison, ESDEs in Northeast China are mainly controlled by the joint influence of the Northeast Asian thermal high and the high-pressure system over Mongolia. Understanding the synoptic behaviours and mechanisms of ESDEs advances our knowledge of such weather hazards to better mitigate their detrimental effects.

Rooftop photovoltaics (RPVs) play an increasingly critical role in global zero-carbon energy transition, yet underexplored is to what extent RPV potential can supply the ever-rising electricity demand under the combined effects of urbanization and climate change. Here, we project that the global RPV potential will grow by 13%—20% from 2015 to 2100 under four different socioeconomic-climate scenarios. This increase is primarily driven by RPV gains associated with urban expansion, although nearly one-fifth of them might be offset by climate-induced losses under high-emissions scenarios. However, future urban densification can drive down per capita RPV potential, which, alongside increasing individual consumption, will diminish RPVs' maximum feasible share in global electricity mix from 117% to 30%—47% by 2100. While simultaneously suffering from more severe and widespread high-emissions-induced losses, many developing countries, particularly the least developed, will undergo stronger urban densification, which shifts their RPV electricity supply-demand ratios from general surpluses over 200% to deficits much larger than those in wealthier countries. Given their reliance on RPVs for access to clean electricity, our results highlight the urgent need for strategic interventions, including embedding climate action into urban planning and accelerating global collaborative technological advancements, to mitigate these disproportionate impacts and foster an equitable energy transition.

Extreme heat and particulate matter (PM2.5) pollution are among the deadliest environmental hazards that threaten humans and ecosystems. Their co-occurrence, known as compound heat-pollution events, can amplify risks far beyond those posed by either hazard alone, but their global patterns and physical mechanisms are yet to be understood. Here, we present a global assessment of compound heat-pollution events from 2003 to 2020 at 1-km fine scale. We identify two hotspots: Sub-Saharan Africa and the Indus River Valley, where compound heat-pollution events occur most frequently. These compound events are typically associated with clear and dry conditions characterized by increased solar radiation and reduced precipitation, humidity, and cloud cover. Notably, during compound events, there is a pronounced high-pressure anticyclone appearing in the Indus River Valley, while weaker atmospheric circulation changes appear in Sub-Saharan Africa. Our findings advance understanding of compound heat-pollution events and support improved risk assessments and regional adaptation strategies.

Heatwaves have become more frequent and intense under anthropogenic climate change, with profound implications for both natural ecosystems and human systems. However, the transboundary migration of heatwaves over China remains unclear, overlooking the significant influence of exogenous heatwaves—those originating outside the region—hampers accurate predictions and effective mitigation strategies to deadly heatwaves. Here we assess the impacts of exogenous heatwaves on heat risks across China using a Lagrangian tracking approach. Surprisingly, our results reveal that 42.7

Near-surface atmospheric moisture is a key component of the climate and environment systems, exerting significant influences on both nature and human beings. However, existing moisture data are often limited by sparse observations and low spatial/temporal resolution, which restricts their applicability at fine scales, particularly in populated and urbanized regions with strong moisture variability, such as the North China Plain (NCP). Here, we construct a high-resolution (daily and 1 km) near-surface atmospheric moisture index collection comprising six different indicators over the NCP during 2003-2020 (HiMIC-NCP). HiMIC-NCP is generated by the Light Gradient Boosting Machine (LightGBM) algorithm by integrating meteorological observations and multiple covariates, including 2-meter air temperature, land surface temperature, water vapor, topography, and population density. The dataset exhibits a high accuracy with R² values ranging from 0.879 to 0.988, and mean absolute error and root mean square error remaining within reasonable ranges. The dataset also exhibits high consistency with ground observations across spatial and temporal regimes, demonstrating its robustness and reliability, and thereby provides a high-quality foundation for fine-scale climate change assessment, agricultural management, and public health studies.

Heat extremes are intensifying under climate change, yet these events are traditionally identified using daily temperature metrics, which fail to capture the full dynamics of heat events. Here we present a global assessment of hourly heat extremes (HHEs) in summer, and show that HHEs are emerging worldwide and exhibit marked unequal exposure across regions and generations. Under a high-emissions scenario, global HHEs are projected to increase fourfold and their population exposure to grow sixfold by late century. Each hot day will gain about four hot hours, and a further three hot hours will occur on non-hot days, which daily metrics overlook. Low- and middle-income countries bear more than three-quarters of the current and future heat exposure, and successive generations, particularly in low-income countries, face much higher lifetime exposure than earlier cohorts. These findings underscore the urgent need for hourly-scale risk assessments, public preparedness and designing equitable strategies in a warming world. The authors quantify the global emergence of hourly heat extremes (HHEs), highlighting exposure overlooked by daily metrics. HHEs will increase fourfold by the end of the century under high emissions, with low- and middle-income countries, as well as successive generations, facing disproportionate exposure.

Very-High-spatial-Resolution (VHR, <= 5 m) remote sensing products (e.g., land cover maps) are critical for providing detailed and scalable information to support broad applications (e.g., urban, agriculture, ecology, and forestry). However, accurate VHR products require imagery that is both spatially detailed and spectrally rich, which is rarely met by a single sensor. Globally available PlanetScope (eight 3-m bands) and Sentinel-2 (thirteen 10/20/60-m bands) are complementary in this regard, making them ideal for synergistic use. Conventional spatial-spectral fusion approaches, such as pansharpening and hypersharpening, however, are tailored to specific sensor modalities and thus ill-suited for blending multispectral images from these two constellations. To bridge this gap, we advance the concept of Multispectral-to-Multispectral sharpening (M2Msharpening) and propose RASSFM 2.0, an enhanced M2Msharpening model over the original Robust and Adaptive Spatial-Spectral image Fusion Model (RASSFM 1.0; Zhao and Liu, 2022) for fusing PlanetScope and Sentinel-2 imagery. RASSFM 2.0 incorporates three key improvements: (1) radiometric harmonization to align PlanetScope spectra with Sentinel-2, (2) inter-band sharpening to downscale 20-m Sentinel-2 bands to 10-m, and (3) all-band fusion to improve accuracy and efficiency. Quantitative and visual assessments across five global fusion sites with representative landscapes confirmed the superior performance of RASSFM 2.0 regarding spatial clarity, spectral fidelity, and processing time. Its practical utility was further validated through land cover classification at three larger classification sites with complex land covers, where a random forest classifier based on the RASSFM 2.0-fused image achieved the highest area-adjusted overall accuracy (90.97 +/- 0.08%), outperforming PlanetScope-only (85.04 +/- 0.10%), Sentinel-2-only (85.65 +/- 0.10%), PlanetScope-Sentinel-2 stacked (87.06 +/- 0.09%), and RASSFM 1.0-fused (88.98 +/- 0.08%) results. Notably, RASSFM 2.0 significantly reduces confusion between spectrally similar classes (e.g., sparse herbaceous and bare land) and improves the delineation of spatially complex surface structures (e.g., fragmented and small-sized objects). Furthermore, the contribution of RASSFM 2.0 to land cover classification is concrete in both pure and mixed pixels, particularly for mixed pixels. These findings demonstrate the effectiveness of RASSFM 2.0 in generating synthetic VHR imagery with rich spectra, greatly enhancing land cover classification across broad landscapes. As a transparent and physics-based model, RASSFM 2.0 serves as a robust standalone tool and can provide valuable physical priors to inform or constrain learning-based fusion methods. This work advances both upstream M2Msharpening methodology and its value in downstream applications.

Humid heatwaves, intensified by the compounding effects of extreme heat and humidity, pose severe risks to human health and ecosystems. Although dry heatwaves have been extensively studied, the local physical processes driving humid heatwaves and their differences from dry heatwaves remain unclear. Here, we provide a global assessment of both heatwave types during 1980–2025 using energy budget decomposition and climate diagnostics. Both heatwave types have become more frequent and persistent since the 1980s, with occurrences increasing 0.36 and 0.22 events per decade respectively, especially in monsoon regions. Diabatic heating dominates both types, primarily through enhanced shortwave radiation during dry heatwaves and longwave radiation during humid heatwaves, while humidity contributes over 40% of humid heatwave intensity globally. Compared with climatology, dry heatwaves are characterized by higher VPD (+0.59 kPa), dry soils under clearer skies, whereas humid heatwaves occur with lower VPD (−0.21 kPa), wetter soils under cloudier conditions. At middle to high latitudes, both types are accompanied by weakened westerlies. Humid heatwaves further involve anomalous poleward transport of warm, moist air from tropical oceans, promoting moisture accumulation over land. Our assessment provides a comprehensive understanding of both heatwave types and insights for mitigating their impacts in a warming climate.

Climate change causes widespread increases in the frequency, magnitude, and extent of flood events, which pose increasing threats to societal and natural systems and highlight the urgency for timely and accurate flood mapping. However, previous flood mapping methods often require prior knowledge (such as the timing and location) of flood events that is usually incomplete or even unavailable when studying historical floods. Here we propose a new amplified deviation flood index (ADFI) using the time-series anomaly statistics from the Synthetic Aperture Radar (SAR) data for mapping fully flooded areas without relying on prior knowledge of flood events. ADFI is constructed by considering two fundamentals of flood events: a decrease in backscatter intensity when ground objects are fully flooded and an increase in the variance of backscatter intensity owing to infrequently sudden occurrence of flood events, thus enabling a fast non-prior detection of flood events and extents. The performance of ADFI is assessed in four study areas across different climate zones of the globe, and the assessment shows that the overall accuracies of ADFI in all study areas exceed 93%, with precision >95% and recall >94%. Further comparison with two existing flood indices suggests that our proposed ADFI-based mapping method can improve the overall accuracy by 12.11%-3.97%, precision by 12.59%-10.17%, and recall by 54.32%-6.37%. A time-series flood mapping based on ADFI demonstrates that our proposed method enables a non-prior, precise, and fast detection of flood events and allows prompt monitoring of flood disasters. Our proposed approach enhances the efficiency and scalability of flood monitoring, providing a valuable tool for rapid disaster response and the reconstruction of long-term flood histories across diverse environments and climates.

Rivers and the organisms living within them are highly vulnerable to hot thermal extremes. However, very little is known about river heatwaves, consecutive episodes of anomalously high temperature in rivers, and how they may evolve under climate change. Here we show that river heatwaves will become more intense and more persistent globally by the end of the 21st century, with some tropical rivers reaching a persistent year-round heatwave state in the early 21st century. Under the high-greenhouse-gas-emission scenario (Representative Concentration Pathway 8.5), the average intensity of river heatwaves will increase by ~4.2-fold, and the average duration by ~95-fold, relative to the baseline period (1976–2005). Nearly half of the world’s rivers are expected to experience a year-round heatwave state by the 2090 s. Global population exposure to river heatwaves will reach 16.8 billion person-weeks annually, with a disproportionately heavier burden on vulnerable low-income regions, such as the Congo River basin. Emerging persistent river heatwaves may push river ecosystems and aquatic organisms to their resilience limits, causing irreversible changes and widespread impacts. River heatwaves are becoming stronger and longer-lasting globally. Nearly half of the world’s rivers will reach a ‘permanent’ (year-round) heatwave state by the 2090 s under high greenhouse gas emissions scenario, and annual population exposure will reach 16.8 billion person-weeks.

Abstract A pronounced interdecadal increase in summer (June–August) precipitation over southern China has been evident around 1993. Yet, the relative contributions of extreme precipitation (EP) and non-EP to this increase and the physical drivers remain poorly understood, despite their critical implications for water resource management and disaster preparedness. Our analysis shows that both EP and non-EP increased from 1979 to 1992 and from 1993 to 2006. Although EP accounts for only 26% of climatological rainfall, it explains a disproportionate 43% of the total increase, indicating an increased fractional contribution of EP to total precipitation amount. Notably, these increases are driven primarily by more frequent rainfall events rather than greater event intensity. The enhanced frequency of monsoon and tropical cyclone precipitation events is mainly caused by strengthened monsoon circulation and increased tropical cyclone passages over southern China, respectively, both tied to more occurrences of westward extensions of the western Pacific subtropical high. These findings highlight that circulation-driven increases in event occurrence frequency, rather than rainfall intensification, have governed the interdecadal precipitation increases in southern China. The results underscore the importance of considering precipitation occurrence frequency and intensity and their links to large-scale circulation for improving the simulation, projection, and interpretation of regional hydrological changes. Significance Statement Southern China has experienced a prominent interdecadal increase in summer rainfall around 1993, yet the processes driving this shift have remained unclear. This study shows that the rise was dominated by more frequent rainfall events rather than stronger ones. These changes are primarily driven by enhanced monsoon circulation and increased tropical cyclone passages associated with westward extensions of the western Pacific subtropical high. The results highlight the dominant role of circulation-driven frequency changes in shaping regional hydroclimate. Recognizing this mechanism is crucial for enhancing the reliability of regional climate projection and improving water resource management.

Human-perceived temperature (HPT) describes the combined effects of multiple meteorological factors on human body. However, the relationship between HPT, local climate zones (LCZs), and extreme weather events remains unclear, especially for rapidly urbanizing regions including the North China Plain (NCP), one of the most populated regions vulnerable to heat stress. Here, we examine the HPT changes associated with LCZ and temperature extremes by taking NCP as an example. We show that HPT in built-up areas of NCP is warmer than that in natural surfaces, with an average summer heat index of 27.69 degrees C and 27.26 degrees C, respectively. Mid- and highrise buildings exhibit higher HPTs than low-rise. This difference is even larger in denser building agglomerations (0.80 degrees C in compact areas versus 0.75 degrees C in open zones). Urban thermal environment is more comfortable in greenery, particularly tree-covered areas. A comparison between normal and extreme weather conditions reveals a remarkable cooling effect by urban greenery. Nevertheless, during extreme heat, urban trees may have diminished cooling and potentially exacerbate humid heat threat, likely via increased water vapor by evapotranspiration. Under extreme conditions, LCZs 7 and 10 demonstrate high HPT variability and vulnerability. These findings provide valuable insights for improving urban climate resilience, landscape planning, and sustainable development.

Temperature projections from general circulation models (GCMs), serving as an important approach of understanding future global warming, are essential for developing adaptation and mitigation strategies of climate change. However, the coarse spatial resolutions (~1-3°) limit their effectiveness at fine-scale (e.g., intra-urban) research. Here, we produced MoCHAT, a global monthly CMIP6-downscaled high-resolution (1 km) near-surface air temperature dataset. We utilized delta downscaling method to generate MoCHAT based on NEX-GDDP-CMIP6 and WorldClim. MoCHAT encompasses mean, maximum, and minimum air temperature of 16 GCMs. It covers both the historical period (1950-2014) and future scenarios (2015-2100) under three Shared Socioeconomic Pathways (SSPs) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Validation with meteorological station observations and existing high-resolution climatic datasets showed that the mean absolute errors for these variables range from 1.60 to 2.38 K and overall biases below 2.0 K. With sufficiently long span and fine resolution, MoCHAT breaks through data resolution limitations and provides solid support for global fine-scale heat risk research.