From July 29 to August 1, 2023, the North China region experienced a rare extreme rainstorm event (referred to as the “23.7” rainstorm), which triggered widespread flood disasters. This study systematically analyzed the water vapor transport characteristics and key driving mechanisms of this rainstorm based on ground meteorological station observation data, ERA5 reanalysis data, S-band Doppler radar data, and a qualitative Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The research results show that persistent southeasterly moisture transport from the Western Pacific Ocean and the South China Sea was strongly associated with sustaining the rainstorm in the Hebei region. This transport established a significant moisture convergence center, providing an abundant moisture source for the extreme precipitation. At upper levels, the southwest airflow formed around the western Pacific subtropical high strengthened vertical divergence over the rainstorm area. This pronounced upper-level divergence acted as a dynamic driver for deep ascent, maintaining the vertical circulation necessary to transport high- θ_e air from the lower troposphere into the storm core, thereby prolonging the duration of heavy precipitation.During this rainstorm, the 55 dBZ strong radar echo centers of the convective system exhibited significant vertical development, extending into the upper troposphere. Although radar-derived heights serve primarily as qualitative indicators due to influences such as hydrometeor population, beam geometry, and attenuation effects, these elevated echo tops are consistent with the presence of exceptionally vigorous updrafts that facilitated the rapid lofting of hydrometeors and intensified surface rainfall. Additionally, trajectory simulations qualitatively identified the primary moisture transport routes for this heavy rainfall event. The primary driving mechanism behind the “23.7” rainstorm was the synergistic interaction among sustained and abundant horizontal moisture transport, intense vertical lifting within the storm region, and a robust upper-level divergence field. The dynamic coupling of these factors was strongly associated with prolonged convective activity. Moreover, a continuous moisture influx maintained a near-saturated thermodynamic profile, which significantly enhanced precipitation efficiency. Combined with the quasi-stationary nature of the system, this thermodynamic environment was critical in sustaining the massive rainfall totals. Further investigation into the triggering mechanisms of vertical water vapor transport is crucial for enhancing the forecasting capabilities of localized rainfall, particularly in mountainous regions, and for improving the accuracy of disaster risk assessment. This study advances the understanding of extreme precipitation formation mechanisms in North China, providing scientific support for optimizing numerical weather prediction models and strengthening early warning capabilities for extreme weather events.

Due to the complex interaction between varied pollutant emission sources and atmospheric circulation patterns, achieving reliable air quality prediction poses a formidable challenge. Consequently, the changes in PM2.5 and O3 pollution under future climate change scenarios remain largely unknown, particularly in regions that are frequently affected by severe air pollution, such as the Northern China urban agglomeration (NCUA). Here we developed an Integrated Graph Neural Network (IGNN) model that, trained by historical meteorological and emission data, is able to predict future PM2.5 and O3 concentrations over the NCUA. The IGNN model contains nodes and edges, with historical station observations being the graphical nodes, while spatiotemporal features of air quality variables, meteorological properties and emission information are defined as attributes of the nodes and edges. The results demonstrate that the IGNN model effectively captures the variability of historical PM2.5 and O3, outperforming other state-of-the-art methods in accurately predicting air quality variability. In high carbon emission scenario with varying air pollutant strategies, all IGNN model simulations show a significant decrease of average concentration of PM2.5 (the rate of decline ranges from-0.14 to-0.37 mu g m(-3 )year-1, p < 0.05), Conversely, there is a significant increase in average concentration of O3 (+0.07 to +0.22 mu g m(-3 )year(-1), p < 0.05). Our findings highlight the risk posed by elevated O-3 pollution levels under high carbon emission scenarios in the NCUA. This underscores the critical necessity for a coordinated approach that integrates air quality control with climate change mitigation efforts.

Abstract The yak ( Bos grunniens ) serves as an exceptional model for studying high-altitude adaptation mechanisms due to its evolutionary success in the hypoxic environment of the Qinghai-Tibet Plateau. Previous research has largely focused on genetic and physiological traits of yaks; however, the interactions between rumen microbiota and host physiology under hypoxic conditions are poorly understood. As the largest digestive organ in ruminants, the rumen and its microbiota play a central role in digestion and host nutrition. In this study, a comparative analysis of digestive metabolism and rumen microbiota was carried out in yaks and cattle ( Bos taurus ) under two distinct atmospheric oxygen scenarios: baseline (2,200 m) and hypoxic (3,800 m). Our findings reveal that yaks have developed unique microbial strategies to cope with energy deficits in hypoxic stress. These strategies include a shift in rumen microbiota toward amino acid degradation, providing more available energy substrates for host utilization, and enhanced long-chain fatty acid biosynthesis, enabling more efficient energy storage and utilization. This improves energy acquisition in yaks despite their reduced nutritional intake. However, this metabolic adaptation comes at a physiological cost - reduced microbial crude protein (MCP) synthesis, leading to elevated ruminal NH 3 -N levels, and increased fatty acid metabolism and urea cycle activity contributing to hepatic stress. Our results showed that under high-altitude conditions, yak MCP synthesis decreased by 47.3%; and ruminal NH3-N and serum ALT (a hepatic stress marker) increased by 147.2 and 19.7%, respectively. This study presents evidence of potential metabolic trade-offs in high-altitude adaptation, indicating that yaks may optimize microbially mediated energy production at the cost of liver health. These insights deepen our understanding of host-microbiome coevolution mechanisms in extreme environments and highlight biological costs associated with adaptation to high altitudes.

Abstract Flood losses and risks in China are shaped by its monsoon climate, topography, and the spatial distribution of socioeconomic development. The convergence of the Eurasian plate with the Pacific and Indian Oceans creates China’s strong monsoon climate, which profoundly affects the spatiotemporal distribution of heavy rainfall. This impact is further amplified by the increasing intensity and frequency of extreme rainfall events driven by climate change. Moreover, China’s unique three-step topography—with the highest step in the northwest and the lowest in the southeast—fundamentally affects runoff processes, ultimately producing a spatial pattern of flood hazard that is higher in the southern and eastern regions and lower in the northern and western regions. Lastly, urbanization, land-use changes, and other human dimensions have altered the characteristics of flood losses and risks in China, primarily by changing flood hazard and exposure. Flood risk estimates based on the height above the nearest drainage model indicate that eastern coastal areas, including the North China Plain, the Huai River basin, and the Songnen Plain, are especially prone to flooding. When river water levels rise by 3 meters compared to the low-flow condition, the maximum potential inundation area of China reaches 1.10 million km², which is close to the long-term annual average inundation area and the extent of China’s 100-year floodplain. Approximately 17% of China’s population, 16% of its GDP, and 22% of its built-up areas are concentrated within this zone. With the increasing frequency and intensity of extreme rainfall events, socioeconomic development in China’s flood-prone areas is being subjected to high flood risk. Flood loss data over the period from 1976 to 2024 indicate that China has achieved remarkable progress in flood management, leading to a notable reduction in its global share of flood-related deaths and affected populations, as well as lower mortality and affected rates. These efforts have helped reduce global flood loss. However, the cumulative loss of human life from small-scale, frequent flood events in China has exceeded that from extreme, sporadic floods. While attention to extreme floods remains essential, greater focus should also be placed on the impacts of smaller and more frequent flood events.

Balancing grassland conservation with livestock production often involves trade-offs, yet synergy can be achieved through strategic adjustments in herder behaviors for livestock husbandry, particularly in regions dominated by traditional extensive grazing practices where investigations into optimal solutions that reconcile ecological and economic benefits remain limited. In this study, the grassland‒livestock balance at the county level in Qinghai Province, a relatively high-elevation pastoral landscape with extensive grazing and grassland degradation, is systematically explored, and the impacts of optimizing off-take schedules and enhancing supplementary feeding on the efficiency of livestock husbandry are investigated. An integrated evaluation model for assessing the dynamics between forage supply and demand was developed, and three management strategies and five simulated scenarios were used to investigate the impacts of adaptive managements. Our findings revealed that traditional livestock herding practices, characterized by excessive grazing and no (or limited) supplementary feeding during cold seasons, resulted in substantial forage waste and exacerbated grassland degradation. The adoption of early off-take practices could increase production efficiency by as much as 36%, whereas the integration of supplementary feeding could increase production efficiency by 118%, particularly in high-elevation pastoral communities. However, considering the substantial costs of feed transportation, the integrated supplementary feeding mode, while increasing livestock production relative to the early off-take strategy, ultimately reduced the potential economic benefits. This study highlights the need for institutional reforms that provide policy incentives for behavioral transitions among herders, coupled with improvements in transport infrastructure and market accessibility to increase the supplementary feed supply.

Abstract In a warming climate, the co‐occurrence of drought and heat events increasingly threatens the global wheat yield and food security. However, changes in compound dry and hot events (CDHEs) during the global wheat growing season and their impacts on yield remain largely unknown. Using daily ERA5 reanalysis data, multiple drought indicators including the standardized precipitation index, standardized precipitation evapotranspiration index, standardized soil moisture index (SSI), and heat indicators the standardized temperature index and standardized soil temperature index (SSTI), are compared to assess the evolution of CDHEs and their impacts on wheat yield in major wheat‐producing regions (1981–2020). The results indicate significant increases in the frequency, duration, and intensity of global CDHEs, with the most pronounced increases experienced in arid and semiarid regions (Eastern Europe, Central Asia, and Turkey). SSI–SSTI was most sensitive to frequency changes, SPEI–SSTI best captured intensity and duration, and SPI–STI provided conservative estimates. These trends reflect regional hydrothermal conditions, land–atmosphere interactions, and agricultural management. When CDHEs account for more than 10% of the growing season, over 70% of wheat areas experienced negative yield anomalies with an average anomaly of −6.3%; Canada, Australia, and Central Asia were severely impacted, whereas highly irrigated regions (e.g., China and India) were less impacted. Indicator combinations incorporating evapotranspiration and soil moisture (SPEI–SSTI, SSI–SSTI) were most strongly correlated with yield anomalies, highlighting their effectiveness for compound stress detection. This study emphasizes the importance of multi‐indicator assessments and regional adaptations for developing climate‐resilient agricultural strategies.

High-resolution Earth observation (EO) is critical for tracking spatially heterogeneous sustainable development goals (SDGs), such as cropland dynamics and urban expansion. However, persistent limitations in spatiotemporal continuity (satellite revisit gaps) and cost-efficiency (prohibitive pricing of commercial <2 m data) hinder its scalability. While deep learning-based single image super-resolution (SR) techniques offer a potential solution, their quantitative equivalence to native high-resolution data and the generalizability across geographies remain unproven. Here, we demonstrate that AI-powered SR can systematically transform freely available 10-m Sentinel-2 imagery with visible (RGB) and near infrared (NIR) bands into 2m-resolution images with RGB-NIR bands while preserving spectral-temporal fidelity. Specifically, we trained a geospatially constrained transformer-based SR framework (GeoSR) with 3.15 million km & sup2; of co-registered Gaofen-1/6 and Sentinel-2 pairs, achieving near-native performance with <1% F1-score loss in critical applications: cultivated land parcels mapping (F1-score = 0.84 vs. 0.85 for native Gaofen-1/6), urban footprint extraction (0.82 vs. 0.81), and fine-grained land use and land cover classification (0.43 vs. 0.43). Notably, the geospatial module enables large-scale generalization, retrospectively reconstructing decade-long environmental dynamics from historical archives - an unprecedented capability unattainable solely through launching new satellites. We further demonstrated operational scalability through two large-scale implementations: mapping 6.09 million hectares of agricultural parcels of the entire Anhui Province, China, and extracting 9866 km & sup2; of building footprints across 87 C40 coastal cities. As a free-to-access platform (www.sr-earth.org), GeoSR significantly reduces high-resolution data costs compared to commercial alternatives. AI-powered SR is not merely an image enhancement tool but a scientifically valid EO data source, particularly transformative for specific SDG monitoring in low/middle-income regions where native HR data scarcity impedes evidence-based policymaking.

Advocating for low-carbon residential energy usage is crucial for attaining China's carbon neutrality objectives. This study utilizes a stratified survey of 741 households in Zhongshan to investigate the interplay between educational attainment and environmental responsibility awareness in influencing the willingness to pay extra for energy-efficient appliances. It also examines the spatial and behavioral dynamics underlying these decisions. The results indicate that higher education markedly increases investment propensity by enhancing cognitive skills and long-term decision-making capabilities. Conversely, awareness of environmental responsibility may diminish the propensity to invest—especially among households with moderate education—when financial incentives are absent, resulting in a decrease of up to 24%. Education serves as a moderating factor in reconciling the disparity between environmental values and behavioral capabilities. Distinct spatial disparities are evident: households in central urban regions exhibit greater environmental motivation, whereas those in peripheral areas face more significant financial constraints. The hukou status further distinguishes behavior—non-local households exhibit greater responsiveness to educational influences while also being more sensitive to immediate costs. These findings highlight the necessity of spatially customized and socially attuned policies that combine cognitive empowerment with behavioral incentives to effectively promote household green investments and facilitate the transition to low-carbon energy consumption.

Soil organic carbon (SOC) in surface horizons is sensitive to climate variability. We quantified spatial variation in SOC density (SOCD) across Qingzang Plateau (QP) grasslands, identified its controls, and estimated regional stocks. Field surveys (120 soil profiles; 1 × 1 m biomass plots), laboratory measurements (dry-combustion SOC; undisturbed-core bulk density; laser-diffraction texture), and geospatial datasets (MODIS MOD17A3HGF NPP; CRU TS climate) underpinned multivariate analyses (Mantel, variance partitioning, SEM). Soils were sampled to 0–60 cm, and SOCD is reported as kg m-2 60 cm-1. Alpine meadows exceeded alpine steppes in above-ground biomass carbon density (AGBD), below-ground biomass carbon density (BGBD), and SOCD. Alpine meadows means were 0.043kg m-2, 0.172kg m-2 60 cm-1, and 3.87 kg m-2 60 cm-1; SOCD was ~18× plant-biomass carbon, confirming soil as the dominant pool. Mechanistically, SOCD reflects the balance of carbon inputs (plant productivity and below-ground allocation), soil stabilization (texture/mineral surface area, Ca-/pH-mediated aggregation, moisture retention, and rock-fragment fraction δ), and climate-regulated turnover (MAP/MAT controls on microbial decomposition); these pathways operate in both meadows and steppes, with differing magnitudes. Total SOC stock (0–60 cm) is 23.54 Pg C, with meadows and steppes contributing 59.7% and 40.2%. Biomass carbon is partitioned 61.3%/38.6% above ground and 49.0%/50.9% below ground (meadow/steppe). These results clarify mechanisms of grassland carbon storage and guide conservation and carbon management on the Plateau.

The alpine grasslands of the Qinghai-Xizang plateau (QXP) serve as significant carbon reservoirs, storing approximately 2.5% of global soil organic carbon (SOC). However, the mechanisms governing SOC distribution across different environmental gradients remain insufficiently understood. In this study, we quantified SOC and its labile fractions (DTC, DOC, MBC) across four major grassland types-Alpine meadow (AM), alpine swamp meadow (ASM), alpine steppe (AS), and alpine desert steppe (ADS)-Using 120 soil profiles (0-60 cm) sampled along a 1200 km transect. Our results revealed significant vertical stratification of SOC, with the highest concentrations found in the topsoil (0-10 cm). Alpine meadows (AM) and alpine swamp meadows (ASM) exhibited higher SOC and MBC levels than alpine steppes (AS) and alpine desert steppes (ADS). Soil physicochemical properties, particularly moisture content and underground biomass, were the primary drivers of SOC dynamics, while climate factors, especially temperature and precipitation, negatively influenced labile carbon pools (DTC, DOC). The study also showed that microbial indicators, such as MBC, remained stable in certain ecosystems, suggesting ongoing microbial activity and strong carbon-nitrogen coupling. These findings emphasize the critical role of soil properties in regulating carbon sequestration in high-altitude ecosystems and underscore the vulnerability of carbon stocks to climate change. This research provides new insights into the mechanisms driving soil carbon dynamics in alpine regions, offering important implications for refining carbon cycle models and improving future climate projections

Land use/land cover change can alter the urban surface energy balance, thereby causing regional warming or cooling. In this study, future land use in 2038 for four typical cities in the Yangtze River Basin was simulated using the Patch-generating Land Use Simulation (PLUS) model, and the influence of different land use types on the spatial distribution of urban net biophysical effect was analyzed. The results showed that: (1) According to the future urban expansion scenario (Ur), Chengdu would have a larger urban expansion ratio than Panzhihua, Wuhan, and Nanjing. Compared with 2000-2019, the urban areas of Chengdu would grow by 1.1 times under Ur from 2019 to 2038. (2) Net radiation (Rn) formed a spatial distribution pattern of western (Panzhihua) > Eastern (Nanjing) > Central (Wuhan and Chengdu). The Rn and latent heat flux (LHF) were on the rise overall and would continue to rise (5.4%) in the future. (3) Hot and cold spots of LHF-Rn exhibited clear spatial differentiation and differences in land-use composition, and areas with higher proportions of vegetation and water bodies were more likely to form hot-spot clusters, whereas areas dominated by cropland and with higher proportions of construction land were more likely to form cold-spot clusters. This study can help better understand the effects of urban surface energy budget on regional warming under different land use changes, and provide theoretical guidance for coping with urban climate change.

Dust storms represent a serious global environmental issue due to their adverse effects on air quality, crop growth and energy supply, as well as on regional to global weather and climate. However, a solid understanding of the long-term changes in dust storms and their possible causes remain largely unknown. Based on station observations, this study investigates the trend and variability in dust storm frequency over northern China during 1961–2020, and examines their likely relationship with the atmospheric circulation and soil moisture changes. The results show that the annual and seasonal mean dust storm frequency declined significantly over 1961–2020 across the northern China, particularly in northwest China and the central part of northern China. We reveal that the weakened pressure gradient between western Siberia and northern China in the low troposphere leads to a decline of dust transport from Central Asia and Mongolia to northern China. The physically-based dust emission model simulations confirm that decreased surface wind speed and increased soil moisture can effectively diminish local dust emissions over northern China and Mongolia. Therefore, variations in pressure gradient and surface conditions are key drivers of the observed decline in the dust storm frequency over northern China. This study highlights the key role of large-scale atmospheric circulation and local conditions in regulating the dust storm activity, and has crucial implications for the dust storm mitigation strategies.

Livestock snow disasters occur when heavy snow covers grasslands, preventing livestock from accessing forage and exposing them to extreme cold and starvation. Reducing the risk of such disasters requires understanding their mechanisms and accurately assessing losses. However, the role of hypoxic environments in exacerbating livestock losses during snow disasters in the Qinghai Plateau (QP) remains poorly understood, hindering accurate loss and risk assessment. This study addressed this gap by developing a vulnerability model to predict livestock mortality rates under hypoxic environments. We systematically evaluated livestock losses caused by snow disasters across the QP over 1983–2020 by incorporating snow hazard data along with natural environmental and socio-economic variables under a fixed oxygen environment assumption. Furthermore, we quantified the amplified effect of hypoxic environments on livestock snow disasters by applying a control variable approach. The results indicated that the annual mean livestock mortality rate caused by snow disasters across the QP during 1983–2020 was 2.00%, with 39.50% of these losses attributed to hypoxic environments. Large livestock losses were primarily concentrated in the southern and south-eastern QP, whereas sheep losses were more severe across the central, southern, south-eastern, and north-eastern regions. Significant decreasing trends (p < 0.05) were observed in large livestock losses (−9.74 × 104 standard large livestock units per decade) and sheep losses (−1.75 × 105 standard sheep units per decade) across the QP. These declines were driven by the warming climate, improved vegetation conditions and enhanced prevention capacity, which collectively reduced livestock vulnerability. The results provide critical insights for decision-making aimed at mitigating livestock snow disasters and promoting sustainable pastoral development in the QP under climate change.

Streamflow generation in cold and arid regions is more complex and sensitive to climate warming than other climatic zones. This study investigated the responses of streamflow components to future climate change (2021–2050) in the headwater catchment of the Manas River in northwest China. We employed an integrated modelling framework that combined trend-preserving bias-corrected outputs from five Regional Climate Models (RCMs) of the CORDEX-East Asia project with Snowmelt Runoff Model (SRM) simulations. The SRM exhibited strong performance in streamflow simulation (Nash-Sutcliffe efficiency > 0.82), while the bias correction significantly enhanced the reliability of climate projections, reducing precipitation biases by 50–90

Over the past two decades, the Guanzhong Plain Urban Agglomeration (GZPUA) has undergone pronounced land use and land cover change (LUCC), and the associated alterations in surface energy budget (SLH) have profoundly influenced the regional thermal environment. However, understanding of the seasonal differences in the thermal environment remains limited. In this study, SLH analysis and machine learning methods were integrated to quantify the spatiotemporal variations in surface energy factors and thermal environment during 2003-2022. The results showed that the minimum multi-year mean value of the regional SLH during 2003-2022 was 60.29 W/m(2), that is, S-nr - LE - H > 0. This pattern was consistent with the spatiotemporal distribution of high and low land surface temperature (LST) values, with higher values in the central and eastern parts and lower values in the southwestern part of the region. This indicated that net radiation (S-nr) input generally exceeded the combined dissipation by sensible and latent heat fluxes, leaving a certain amount of residual energy at the surface for heat storage and accumulation, although the overall warming trend showed a slowing tendency. Both surface energy factors and the thermal environment exhibited similar seasonal spatiotemporal patterns. In spring, summer, and autumn, the thermal environment was mainly concentrated in the central and northeastern parts of the study area, whereas in winter it was mainly distributed in the central and southern regions. The thermal effect was most pronounced around Xi'an, and the thermal environment zone gradually shifted toward the southeast over time. Seasonal comparisons further indicated marked differences in the partitioning of net radiation, sensible heat flux, and latent heat flux among land use types, reflecting distinct thermal responses under different hydrothermal conditions. SHAP analysis further revealed that the effects of surface energy factors on the regional thermal environment were strongly nonlinear and exhibited clear seasonal threshold responses. At the annual scale, H and LE were the dominant explanatory factors, whereas the energy factors controlling thermal environment variations differed across seasons. These findings highlight the importance of SLH in shaping the thermal environment under land use change and provide a scientific basis for land use optimization and thermal environment regulation in arid and semi-arid urban agglomerations.

Dust storms represent a major environmental challenge in northern China, adversely affecting air quality, agricultural productivity, and energy supply. However, the drivers behind recent changes in dust storm activity remain poorly understood. By analyzing 39 years of dust storm observations (957 stations), remote sensing, and reanalysis data (1982–2020), we document a significant decline in annual dust storm frequency (−0.490 days·decade⁻¹; p < 0.05), most pronounced in northwestern China. Concurrently, vegetation cover expanded (annual NDVI increase: 0.100 decade⁻¹), exhibiting a strong negative correlation with dust activity (r = −0.616; p < 0.01). Sensitivity experiments conducted with the physically-based Dust Emission Model (DuEMv1) further indicate that enhanced vegetation cover weakens dust activity, suggesting that vegetation greening plays a key role in suppressing dust storms. This also suggests that vegetation greening has the potential to mitigate dust storms in dryland regions, with implications for ecosystem restoration under a warming climate.

The Qinghai-Tibet Plateau, known as the ‘Third Pole,’ plays a key role in the global climate system. This study analyzes the characteristics and drivers of CO2 emissions in the Qinghai-Tibet Plateau, China, based on land use and energy consumption data from 2000, 2010, and 2020. Emissions and their spatial – temporal dynamics were assessed using a land use transfer matrix, emission coefficients, the carbon footprint model, and the Geodetector method. Results show marked land use changes from 2000 to 2020: cultivated land, forest land, and construction land increased by 12.24%, 2.19%, and 251.63%, respectively, while grassland declined by 7.88%. CO2 emissions rose sharply from 155.61 to 448.36 million tonnes. Land use transitions indicate weak coordination between natural and socio-economic systems. Emission growth was driven mainly by primary industry expansion from 2000 to 2010, and by urbanization from 2010 to 2020.