Xueke Li

We employ a semiempirical approach combining climate model simulations and observational temperatures to assess the likelihood of recent global temperature records. Monte Carlo simulations are used to generate global temperature series consistent with combined estimates of forced (anthropogenic + natural) and internal variability derived from observations and CMIP6 multimodel simulations. We find that the El Niño-boosted 2024 global temperature record had a ~12% likelihood of occurrence (a one-in-eight-year event), similar to the prior (also El Niño-boosted) record year 2016 (~14% likelihood). Of the records set during the past three decades, only 1998 is found to have been truly anomalous, with a ~2.5% likelihood of occurrence. Each of these records is found to have been nearly impossible in the absence of human-caused warming.

Understanding historical and future surface soil moisture (SSM) drying is pivotal due to its close links with droughts, heatwaves, and wildfires, yet debates regarding its evolution persist. In this study, we leverage advanced deep learning techniques to fill gaps of remote sensing‐based SSM data during 1983–2020 and therefore use these gap‐filled observations to constrain SSM estimates from 23 Earth System Models (ESMs) during 1901–2100. Our enhanced observations reveal that approximately half of Earth's landmass experienced SSM drying over the past four decades. However, in contrast to projections from current‐generation ESMs, observation‐constrained simulations indicate a less pronounced drying trend in dry‐wet transitions and monsoon margins during 2021–2100 compared to 1901–1980. Current ESMs may overestimate SSM drying in these regions, likely due to their limited representation of soil moisture‐atmosphere feedback. These findings highlight the need to integrate remote sensing and artificial intelligence into ESMs to improve projections of future droughts and their socio‐economic consequences.

A storage-aware water-scarcity risk assessment framework coupling satellite remote sensing, geospatial datasets with the IPCC exposure-hazard-vulnerability (EHV) paradigm was designed to evaluate the spatiotemporal dynamics of global water scarcity risk over the past two decades. To achieve this, a performance-weighted ensemble machine learning approach was employed to reconstruct long-term terrestrial water storage (TWS) from satellite observations, augmented with glacier-mass calibration to improve reliability in cryosphere-affected regions. Global water withdrawal dataset was generated by integrating remote sensing, geospatial dataset, and machine learning to mitigate the dependency of parameterized land surface hydrological models and enable consistent risk mapping. Satellite-derived results reveal obvious TWS declines in Asia, Northern Africa, and North America, particularly in irrigated drylands and glacier-dominated regions. EHV paradigm and big datasets further identified high-water scarcity risk in Asia and Africa, especially in agricultural regions. Water stress has intensified in Africa over the past two decades, while a decreasing trend is observed in parts of Asia. Vulnerability levels in Asia and Africa are approximately eight times higher than those in other global regions. Results reveal a strong connection between water stress and socioeconomic factors in Asia and Africa, reflecting global disparities in water resource availability.

Terrestrial water storage (TWS) in China, with the world's largest irrigated expanse and extensive mid‐low latitude glaciers, is essential for effective water resource management and socioeconomic risk adaptation. However, the responses of TWS to human intervention and climate change, both during historical periods and under future scenarios, remain inadequately quantified. We reconstruct and project long‐term TWS using a data‐driven framework that integrates remote sensing, Earth system model (ESM) and machine learning. Our reconstructed record reveals an amplified TWS decline in China's drylands and a moderate yet persistent TWS reduction in glacier regions during 1985–2015, accentuated since the 21st century with a 13% increase in affected areas. TWS changes in drylands are primarily attributed to human irrigation (39%) and precipitation (24%), with the impacts of irrigation magnified by 9%–12% during drought. Humid basins show a moderate TWS response to irrigation and precipitation, modulated by intricate but unexplored interactions between atmospheric drivers, glacier‐snow dynamics and underlying hydrological processes. Such discrepant response highlights the necessity for region‐specific water resource management strategies: northern drylands should prioritize optimized irrigation practices while southern humid basins would benefit from enhanced adaptation to climate variability. Projections from nine ESMs indicate a likely amplification of TWS decline (13%–43%) in drylands and glacial zones by mid‐century if maintaining current human intervention levels. Our findings emphasize the need to reassess climate change‐induced water scarcity and refine human management regulations, particularly as existing strategies may be overlooking broader sustainability challenges in a warming climate.

Nor’easters are coastal extratropical cyclones that feed upon both thermal contrasts (meridional and land-ocean) and oceanic latent heat release, causing them to intensify along the U.S. East Coast. With central pressures that sometimes rival those of tropical cyclones, they represent a significant coastal hazard and are often associated with strong winds, heavy snowfall, disruption, and damage. While interest in studying the impacts of climate change on storm behavior is growing, nor’easters have historically received far less attention than tropical cyclones, largely due to challenges in documenting and categorizing these storms combined with the relatively short observational record. Here, we address these challenges by employing a cyclone tracking approach in concurrence with long-term reanalysis data to create a reliable historical database of these storms. We find a significant increasing trend in the maximum wind speeds of the most intense (>66th percentile) nor’easters. We also observe an increasing trend in hourly precipitation rates associated with these storms. Such changes have profound implications for coastal cities and shorelines, increasing the risk of coastal flooding and erosion.

Antarctic sea ice plays many crucial roles in the physical environments and ecosystems of Antarctica and the Southern Ocean. In this study, we synthesize the physical, biogeochemical, ecosystem, and societal impacts of summers with extreme low Antarctic sea-ice coverage. These extreme events result in the loss of multiyear landfast ice and changes in sea-ice seasonality. Following extreme low sea-ice events, we find surface warming of the Southern Ocean and changes to the formation rate of Antarctic Intermediate Water, likely affecting heat and carbon uptake. Ice-shelf calving is negatively correlated with sea-ice area, so that years with less sea ice show increased calving. Prolonged open water affects the magnitude and seasonality of surface-phytoplankton blooms. The impacts on higher trophic levels are species-specific and occur through habitat loss and changes to prey availability. Extreme sea-ice lows will adversely impact krill, a foundational prey species that relies on sea ice for nourishment and refuge. The loss of stable landfast ice in austral spring and summer hampers Antarctic operations and resupply missions. Understanding the full impacts of recent, and future, sea-ice extremes is of utmost importance and requires an enhanced observational network that spans the physical and ecological systems of Antarctica and the Southern Ocean.

We demonstrate a tripling in the frequency of planetary wave resonance events over the past halfcentury, coinciding with the rise in persistent boreal summer weather extremes. This increase aligns with changes in the underlying climate conditions favoring these events, including amplified Arctic warming and land–sea thermal contrast. We also observe increased prevalence of resonant amplification events following the mature phase of strong El Niño events, suggesting that such events may precondition the mean state conditions in ways that favor large-scale quasi-stationary wave patterns and quasi-resonant wave amplification. Since the impact of anthropogenic warming on quasi-resonant amplification is not well captured by current-generation climate models, it is likely that models are underpredicting the potential increase, indicating even greater risk of persistent extreme summer weather events with ongoing warming.

Riverine and lacustrine shorelines are crucial for human survival and development, but their natural and ecological environments are highly fragile and sensitive. Intensified human activities have placed unprecedented pressure on the shoreline ecosystem of the Yangtze River Basin. This study investigates the degradation of river and lake shorelines and its cascading effects on ecological service functions. Using Sentinel-2 as the primary data source, we analyzed land use/cover changes and ecosystem service values (ESV) in the Huanggang and Taihu sections of the Yangtze River from 2018 to 2022. The supervised classification results using the support vector machine (SVM) algorithm exceeded 95% accuracy. In the Huanggang section, vegetation was significantly converted into cultivated land and built-up areas (−6.17 km²), while in the Taihu section, water bodies were largely transformed into agricultural land (−3.77 km²). In this study, we quantified changes in ESV using the unit area equivalent factor method, adjusted based on net primary productivity, precipitation, and the soil conservation coefficient. The results indicate that the ESV ranking in both sections follows the order: water conservation > environmental purification > biodiversity > soil conservation. From 2018 to 2022, the ESV in the Huanggang section declined due to forest and grassland loss and an increase in bare land. In contrast, ecological restoration and habitat protection policies contributed to an improvement in ecosystem service functions in the Taihu section, with various ESV components increasing as follows: soil conservation (8.79%) > biodiversity (6.67%) > environmental purification (5.98%) > water conservation (5.52%). These findings provide valuable insights for decision-making in the protection and management of the Yangtze River Basin ecosystem.

Over the last decade, the Southern Ocean has experienced episodes of severe sea ice area decline. Abrupt events of sea ice loss are challenging to predict, in part due to incomplete understanding of processes occurring at the scale of individual ice floes. Here, we use high-resolution altimetry (ICESat-2) to quantify the seasonal life cycle of floes in the perennial sea ice pack of the Weddell Sea. The evolution of the floe chord distribution (FCD) shows an increase in the proportion of smaller floes between November and February, which coincides with the asymmetric melt–freeze cycle of the pack. The freeboard ice thickness distribution (fITD) suggests mirrored seasonality between the western and southern sections of the Weddell Sea ice cover, with an increasing proportion of thicker floes between October and March in the south and the opposite in the west. Throughout the seasonal cycle, there is a positive correlation between the mean chord length of floes and their average freeboard thickness. Composited floe profiles reveal that smaller floes are more vertically round than larger floes and that the mean roundness of floes increases during the melt season. These results show that regional differences in ice concentration and type at larger scales occur in conjunction with different behaviors at the small scale. We therefore suggest that floe-derived metrics obtained from altimetry could provide useful diagnostics for floe-aware models and improve our understanding of sea ice processes across scales.

Drought is a critical hydrological challenge with ecological and socio-economic impacts, but its long-term variability and drivers remain insufficiently understood. This study proposes a deep learning-based framework to explore drought dynamics and their underlying drivers across China’s major basins over the past four decades. The Long Short-Term Memory network was employed to reconstruct gaps in satellite-derived soil moisture (SM) datasets, achieving high accuracy (R² = 0.928 and RMSE = 0.020 m³m⁻³). An advanced explainable artificial intelligence (XAI) approach was applied to unravel the mechanistic relationships between SM and critical hydrometeorological variables. Our results revealed a slight increasing trend in SM value across China’s major basins over the past four decades, with a more pronounced downward trend in cropland that was more sensitive to water resource management. XAI results demonstrated distinct regional disparities: the northern arid regions displayed pronounced seasonality in drought dynamics, whereas the southern humid regions were less influenced by seasonal fluctuations. Surface solar radiation and air temperature were identified as the primary drivers of droughts in the Haihe, Yellow, Southwest, and Pearl River Basins, whereas precipitation is the dominant factor in the Middle and Lower Yangtze River Basins. Collectively, our study offers valuable insights for sustainable water resource management and land-use planning.