Achieving sustainable agricultural production is a critical global challenge, yet the spatial inequalities in greenhouse gas (GHG) emissions and their drivers remain poorly understood. Here, we developed a comprehensive provincial-level assessment of China's agricultural GHG emissions and inequality from 2000 to 2019, integrating carbon dioxide (CO2) and non-CO2 gases using region-specific activity data and emission factors. We quantify spatial heterogeneity in emissions by examining per capita, per agricultural value added, and per unit land area emissions, and apply population-, economy-, and land-based Gini coefficients to systematically evaluate emission inequalities. Our results show that China's agricultural emissions remained relatively stable over the study period, fluctuating around approximately 0.94-1.06 gigatons CO2-equivalent, with non-CO2 gases accounting for 86 %93 % of total emissions. Substantial spatial disparities persist across provinces. Per capita emissions are highest in sparsely populated western and northeastern regions, while per unit area emissions are concentrated in eastern provinces. Emission intensity per unit of agricultural value added declined markedly nationwide, indicating significant efficiency gains. Inequality analysis reveals a continuous increase in the population-based Gini coefficient (from 0.20 to 0.35), a comparatively stable economy-based Gini coefficient (from 0.23 to 0.28), and a declining land-based Gini coefficient (from 0.51 to 0.42), highlighting divergent equity dynamics depending on the metric used. These findings reveal that while emission intensity has improved, substantial regional heterogeneity persists. This study provides a nuanced understanding of agricultural emission inequalities and offers valuable insights for policymakers to design tailored mitigation strategies.

How the differential rotation of the Earth's inner core (IC) has changed over time provides insights into the dynamics of the Earth's interior. Analyses of repeating earthquakes (doublets) have yielded different models. Here we present an event-based investigation using individual events from long-term earthquake sequences, which improves temporal coverage over doublet-based approaches and provides spatial resolution for inferring the rotation rate. Results from two pathways, South Sandwich Islands to Alaska (1982-2024) and Kuril Islands to Argentina (1994-2024), reveal a consistent pattern that the IC successively rotated faster than the mantle by about from the early 1980s and decelerated to a near-zero rate around 2000, perhaps slower than the mantle after about 2010. We also provide a new way to calibrate spatial structure and constrain the average rotation rate with improved accuracy. The results are consistent with the multidecadal IC oscillation model, but do not support shorter-term oscillations or bursts.

The densely populated East Asia is vulnerable to precipitation extremes. By utilizing a deep learning downscaled high-resolution (0.1°) dataset CLIMEA-BCUD (Climate Change for East Asia with Bias corrected UNet Dataset), changes in precipitation extremes over East Asia under different emission scenarios are investigated. Evaluation against observations from different sources (i.e., reanalysis-based, in situ-based and satellite-based) shows that CLIMEA-BCUD can reasonably reproduce the spatial patterns of the extreme precipitation indices, although it tends to overestimate CWD (consecutive wet days) in the Indo-China Peninsula. CLIMEA-BCUD exhibits good agreement with the magnitude of the observations and presents an obvious improvement in terms of bias, root mean square error, and correlation coefficient compared with the driving CMIP6 (Coupled Model Intercomparison Project Phase 6) models. The frequency and intensity of precipitation extremes are projected to increase in most parts of East Asia, especially over southern latitudes such as India and Indo-China. More pronounced increases in R10mm are also projected over the Tibetan Plateau. Record-breaking events, even those that break historical records by much higher magnitude, are becoming more frequent in a warmer climate. During 2071–2100, precipitation extremes that break the historical records by two or more standard deviations are three to five times more likely to occur somewhere in East Asia under SSP5-8.5 compared to those under SSP1-2.6. Potential hotspots of such record-breaking precipitation extremes are high-altitude areas such as the Tibetan Plateau, where the probability of experiencing record-breaking R95p, which breaks historical records by at least two standard deviations, is 40

Following the Coordinated Regional Downscaling Experiment East Asia Phase II (CORDEX-EA-II) setting, two regional climate models (RCMs) driven by four global climate models (GCMs) have been used to provide climate change information on surface air temperature and daily precipitation. A trend-preserving bias correction method, quantile delta mapping (QDM), is first validated for the historical period of 1981-2005 and then applied to the future period of 2040-2060 under the Representative Concentration Pathway (RCP) 8.5 scenarios. Results show that QDM is competent in correcting model biases on temperature, precipitation and compound events (CEs, defined as the concurrent occurrence of temperature and precipitation anomalies) for both spatial distributions and annual cycles. For future assessments, a widespread warming is projected over the region, with average temperature changes higher than 1.6 degrees C. Changes in precipitation are more region-variated. Increased precipitation is most significant in the northwestern part of the CORDEX-EA region, and some models also present a decreased precipitation in southeastern China. Climate changes on CEs correspond with the effect of global warming; RCMs present a significant increase in the frequency of hot CEs and an obvious decrease in cold-dry CEs. These results are expected to be useful for future climate assessments and for better understanding of the bias correction technique under climate change situations.

Highly accurate near-real-time satellite precipitation estimates (SPEs) are important for hydrological forecasting and disaster warning. The near-real quantitative precipitation estimates (REGC) of the recently developed Chinese geostationary meteorological satellite Fengyun 4A (FY4A) have the advantage of high spatial and temporal resolution, but there are errors and uncertainties to some extent. In this paper, a self-adaptive ill-posed least squares scheme based on sequential processing (SISP) is proposed and practiced in mainland China to correct the real-time biases of REGC hour by hour. Specifically, the scheme adaptively acquires sample data by setting temporal and spatial windows and constructs an error-correction model based on the ill-posed least squares method from the perspectives of climate regions, topography, and rainfall intensity. The model adopts the sequential idea to update satellite precipitation data within time windows on an hour-by-hour basis and can correct the biases of real-time satellite precipitation data using dynamically changing parameters, fully taking into account the influence of precipitation spatial and temporal variability. Only short-term historical data are needed to accurately rate the parameters. The results show that the SISP algorithm can significantly reduce the biases of the original REGC, in which the values of relative bias (RB) in mainland China are reduced from 11.2% to 3.3%, and the values of root mean square error (RMSE) are also reduced by about 17%. The SISP algorithm has a better correction in humid and semi-humid regions than in arid and semi-arid regions and is effective in reducing the negative biases of precipitation in each climate region. In terms of rain intensity, the SISP algorithm can improve the overestimation of satellite precipitation estimates for low rain intensity (0.2–1 mm/h), but the correction for high rain intensity (>1 mm/h) needs further improvement. The error component analysis shows that the SISP algorithm can effectively correct the hit bias. This study serves as a valuable reference for real-time bias correction using short-term accumulated precipitation data.

Using fourteen global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) downscaled by four statistical downscaling methods, future changes and the associated uncertainty in concurrent long-duration dry and hot (LDDH) events are investigated in China during summer. The downscaling methods include BCSD (bias-correction and spatial downscaling), BCCI (bias-correction and climate imprint), BCCAQ (bias correction constructed analogues with quantile mapping reordering), and CDF-t (cumulative distribution function transform). The downscaling methods can efficiently improve the accuracy over the driving GCMs in terms of spatial variability, bias, and inter-annual variability of LDDH characteristics. Overall, the three quantile mapping based techniques (BCSD, BCCI, and BCCAQ) outperform CDF-t in simulating the spatial and temporal features of LDDH events. In the twenty-first century, all downscaling methods project a consistent increasing tendency for the frequency, magnitude, and total days of LDDH events over most parts of China, with higher increases under RCP8.5 compared to RCP4.5. A substantial increase in spatially contiguous regions simultaneously experiencing LDDH events is seen by mid-century under both scenarios. Changes in the frequency, magnitude, and total days of LDDH events are predicted with high confidence. For most indices, model uncertainty dominates throughout the century and does not change much over time. However, for the projection of temperature magnitude of LDDH events, the dominant role of GCM related uncertainty in the early twenty-first century declines as scenario uncertainty becomes more important towards the end of the century.

Oocyte maturation defect can lead to maternal reproduction disorder. NAMPT is a rate-limiting enzyme in mammalian NAD+ biosynthesis pathway, which can regulate a variety of cellular metabolic processes including glucose metabolism and DNA damage repair. However, the function of NAMPT in porcine oocytes remains unknown. In this study, we showed that NAMPT involved into multiple cellular events during oocyte maturation. NAMPT expressed during all stages of porcine oocyte meiosis, and inhibition of NAMPT activity caused the cumulus expansion and polar body extrusion defects. Mitochondrial dysfunction was observed in NAMPT-deficient porcine oocytes, which showed decreased membrane potential, ATP and mitochondrial DNA content, increased oxidative stress level and apoptosis. We also found that NAMPT was essential for spindle organization and chromosome arrangement based on Ac-tubulin. Moreover, lack of NAMPT activity caused the increase of lipid droplet and affected the imbalance of lipogenesis and lipolysis. In conclusion, our study indicated that lack of NAMPT activity affected porcine oocyte maturation through its effects on mitochondria function, spindle assembly and lipid metabolism.

Near-surface meteorological forcing (NSMF) datasets, mixed observations, and model forecasts are widely used in global climate change and sustainable development studies. For practical purposes, it is important to evaluate NSMF datasets, especially those released latest, and determine their strengths and limitations. In this study, we evaluate the performance of Multi-Source Weather (MSWX) in China over the period of 1979–2016. For comparison, ECMWF Reanalysis version 5 (ERA5), China Meteorological Forcing Dataset (CMFD) and Princeton Global Forcing (PGF) dataset are also evaluated to determine the strengths and weaknesses of MSWX. The following variables are compared with observations over 2400 stations: 2 m air temperature (T2m), 2 m daily maximum air temperature (Tmax), 2 m daily minimum air temperature (Tmin), precipitation (P), and 10 m wind speed (V10). The evaluation is conducted in terms of climatology, inter-annual variations and seasonal cycles. Results show that MSWX reasonably reproduces the spatial pattern of T2m with root-mean-square errors (RMSEs) below 1.12 °C and spatial correlations above 0.97, but underestimates Tmax and overestimates Tmin, with biases ranging from −2.0 °C to 2.0 °C, especially over the North China and Northeast China. Compared with ERA5 and PGF, MSWX can better simulate the inter-annual variations of surface air temperature with high spatial correlations (>0.97) but shows higher RMSEs than PGF. For precipitation, MSWX accurately captures the primary features of precipitation, including significant characteristics or patterns of the precipitation climatology and inter-annual variation. Its inter-annual variation shows low RMSEs ranging from 0.55 mm/day to 0.8 mm/day, compared to ERA5 and PGF. However, regions with abundant precipitation exhibit higher biases. Because the biased Global Wind Atlas (GWA3.1) is used for the wind bias correction of MSWX, MSWX significantly overestimates the annual mean wind speed, but it is consistently well-correlated with observations, with RMSEs less than 1.5 m/s and spatial correlations greater than 0.6 over the period of 1979–2016. This study reveals both the advantages and disadvantages of MSWX, and indicates the need for research into climate change and sustainable development in East Asia.

Compound wind and precipitation extremes (CWPEs) amplify risk to human health, socio‐economic and ecological systems relative to their single extreme meteorological events. Given the rise in weather and climate extremes resulting from global warming, it is crucial to evaluate the ability of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to capture this bivariate compound event and explore projected changes of CWPEs in the future under different climate‐change scenarios—the Shared Socioeconomic Pathway (SSP) scenarios. In this study, we first evaluate 14 CMIP6 models at a global scale using the ERA5 reanalysis data set spanning 1979–2014. Overall, some of the CMIP6 models, especially the multi‐model ensemble mean (MMEM), can reasonably capture CWPEs during the historical period, with more CWPEs in the northern and southern hemispheres during their respective cold seasons. However, the MMEM tends to overestimate CWPEs in some land areas and show underestimation in some oceanic regions. Then we compute projected changes of CWPEs in periods 1 (2041–2070) and 2 (2071–2100) under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios. Low emission scenarios effectively mitigate the long‐term increase in future CWPEs. The occurrence of CWPEs will change significantly with the increase of emissions during period 2, particularly in polar regions. Finally, we quantify the uncertainty for global future projections of CWPE changes. The main sources of uncertainty are internal variability and model uncertainty, but the contribution of scenario uncertainty will increase as time progresses. Overall, our results provide useful information to cope with CWPEs' global impact, emphasizing the importance of incorporating the compound nature of weather and climate extremes in future climate projections.

AbstractPersistent hot and dry conditions could lead to serious impacts on society, economy, and human health. Using statistically downscaled and bias corrected data, we investigate the changes in compound long‐duration dry and hot (LDDH) events and the corresponding socioeconomic exposure over China in transient and stabilized warmer worlds. The transient response is identified with Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 models, while the stabilized response is identified with the Community Earth System Model ensemble. Under 1.5°C and 2°C warming, the LDDH events in China will become more frequent and hotter. Substantial differences are found in LDDH features between transient and stabilized warming. For a given global temperature, the increase in frequency and temperature magnitude of LDDH events over most northern regions is significantly greater in a transient case than in a stabilized climate, while the increase over the southeastern China is substantially stronger under stabilized warming than transient warming. Future population exposure to LDDH events is projected to increase over China. For many regions, the aggregate exposure is two times greater in a transient climate than in a stabilized climate. Under transient warming, changes in LDDH events dominate the increase in population exposure whereas population change has smaller effects below 10%. For a stabilized warmer world, the negative population growth can largely offset the impact of climate change on exposure. Limiting global warming to 1.5°C instead of 2°C can reduce the exposure to LDDH events by 26.1% and 21.8% over China under transient and stabilized warming, respectively.

In recent years, with the popularity of LIDAR, depth cameras and other devices and the development of intelligent robots, high-precision maps, smart cities and other fields, the demand for outdoor scene understanding and environment perception at large scales is also getting higher and higher, and 3D point cloud semantic segmentation technology is precisely one of the research focuses. However, the current 3D semantic segmentation system mainly relies on the use of fully labelled 3D scenes for training. It is time-consuming and costly to fully annotate 10 million or even hundreds of millions of point clouds. Inspired by the weakly supervised semantic segmentation of 2D scenes, state-of-the-art research has also started to use a small number of labels to segment 3D scenes first. However, the outdoor point cloud data in it is large in scale and covers a wide area, which is a great challenge for neural networks to understand the spatial structure at large scales; secondly, the information brought by sparse labels is very important, and the model needs to improve the utilisation of sparse signals.

Compound dry and hot events can cause aggregated damage compared with isolated hazards. Although increasing attention has been paid to compound dry and hot events, the persistence of such hazards is rarely investigated. Moreover, little attention has been paid to the simultaneous evolution process of such hazards in space and time. Based on observations during 1961–2014, the spatiotemporal characteristics of compound long-duration dry and hot (LDDH) events in China during the summer season are investigated on both a grid basis and a 3D event basis. Grid-scale LDDH events mainly occur in eastern China, especially over northeastern areas. Most regions have experienced a pronounced increase in the likelihood of LDDH events, which is dominated by increasing temperatures. From a 3D perspective, 146 spatiotemporal LDDH (SLDDH) events are detected and grouped into 9 spatial patterns. Over time, there is a significant increase in the frequency and spatial extent of SLDDH events. Consistent with the grid-scale LDDH events, hotspots of SLDDH events mainly occur in northern China, such as the Northeast China, North China and Qinghai clusters, which are accompanied by a high occurrence frequency and large affected areas greater than 300 000 km2.

As a representative indicator for the level and sustainability of urban development, urban vitality has been widely used to assess the quality of urban development. However, urban vitality is too blurry to be accurately quantified and is often limited to a particular type of expression of vitality. Current regression models often fail to accurately express the spatial heterogeneity of vibrancy and drivers. Therefore, this paper took Nanjing as the study area and quantified the social, cultural, and economic vitality indicators based on mobile phone data, POI data, and night-light remote sensing data. We also mapped the spatial distribution of comprehensive urban vitality using an improved entropy method and analyzed the spatial heterogeneity of urban vitality and its influencing factors using a plot boundary-based neural network weighted regression (PBNNWR). The results show: (1) The comprehensive vitality in Nanjing is distributed in a “three-center” pattern with one large and two small centers; (2) PBNNWR can be used to investigate the local regression relationships among the driving factors and urban vitality, and the fitting accuracy (95.6%) of comprehensive vitality in weekdays is higher than that of ordinary least squares regression (OLS) (65.9%), geographically weighted regression (GWR) (89.9%), and geographic neural network weighted regression (GNNWR) (89.5%) models; (3) House price, functional diversity, building density, metro station accessibility, and residential facility density are factors that significantly affect urban vitality. The study’s findings can provide theoretical guidance for optimizing the urban spatial layout, resource allocation, and targeted planning strategies for areas with different vitality values.

Three groups of bias correction (BC) methods, which are linear scaling, distributional-based quantile mapping (QM) and empirical-based QM method, have been applied to correct surface air temperature and precipitation from Weather Research and Forecasting model (WRF) simulation within the second phase of Coordinated Regional Downscaling Experiment East Asia (CORDEX-EA-II) framework. WRF simulation and bias-corrected results are evaluated with gridded observations from CN05.1 and APHRODITE. The evaluation is conducted in terms of climate mean, seasonal cycle and extreme indices. Results show that WRF exhibits large biases in simulating surface air temperature and precipitation, which can be significantly reduced by bias correction. Among the three groups of BC approaches, empirical-based QM is the most comprehensive method in adjusting WRF simulation. However, when considering different time scales and regions, distributional-based QM can better correct precipitation higher than 25 mm/day than empirical-based QM, and a simple linear scaling can also show comparable skills in correcting seasonal cycles. Furthermore, based solely on statistical relationships between simulations and observations, bias correction cannot adjust circulation controlled and consecutive events. These results emphasize the importance of BC validation before climate change application. In addition, BC methods should be used carefully, considering regional climate and research subjects.

This dataset contains the data to generate the results in the manuscript "Downscaling and uncertainty analysis of future compound long-duration dry and hot events in China". The downscaled results (BCSD, BCCI, BCCAQ, and CDF-t) are created by R.

The Tienshan Mountains is the main water source and ecological barrier in the central portion of the Silk Road Economic Belt, a new economic development zone with the Asia-Pacific Economic Circle to the east and the European Economic Circle to the west. Production-living-ecological activities in the arid Central Asia region are heavily dependent on water resources mainly recharged from melt and alpine precipitation. Hence, reliable projections of changes in extreme precipitation under global warming are particularly important for the utilization and management of water resources. Based on the downscaled and bias-corrected state-of-art global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we investigate changes in extreme precipitation over the Tienshan Mountains, Central Asia (TMCA) under different levels of global warming (1.5 degrees C, 2.0 degrees C, 3.0 degrees C, and 4.0 degrees C). We specifically assess the robustness of changes and the benefits of limiting warming to 2.0 degrees C as opposed to 3.0 degrees C. Compared with the reference period (1976-2005), a robust change in extreme precipitation across the TMCA is expected for all warming levels. And the fraction of land faced a robust change also increases with warming levels. Furthermore, there would be a substantial rise in extreme impacts in the TMCA when shifting from increases of 2.0 degrees C to 3.0 degrees C. In a scenario involving a 1.0 degrees C rise (i.e., from 2.0 degrees C to 3.0 degrees C), nearly 85.70 % and 60.19 % of the land in the TMCA will be affected by a robust increase in annual total wet-day precipitation (PRCPTOT) and number of light rain days (RSmm), respectively. In the same scenario, areas affected by robust changes in duration indices (consecutive dry days [CDD] and consecutive wet days [CWD]) will likely be less than 11.59 %. Limiting warming to 2.0 degrees C instead of 3.0 degrees C can avoid a marked increased impacts of about 62.84 %similar to 153.77 % of the change in frequency, intensity, and duration of extreme precipitation.

High‐resolution regional reanalysis is one of the most powerful tool to study local and regional high‐impact weather events, climate extremes and climate change impact. In this study, two high‐resolution Chinese Regional Reanalysis (CNRR) datasets with a resolution of 18 km over 1998–2009, which are produced using the Gridpoint Statistical Interpolation (GSI) data assimilation system and spectral nudging (SN) technique, were assessed. The reliability of the surface and upper air variables of the CNRR datasets was evaluated by comparing with in situ observations and the European Centre for Medium‐Range Forecasts (ECMWF) ERA‐Interim (ERAIN) and ERA5 global reanalysis dataset. The evaluation of climatological and seasonal spatial distribution of near‐surface variables shows that the CNRR can provide more accurate near‐surface variables than the driving ERAIN global reanalysis, and CNRR‐GSI shows advantages against ERA5 in representing near‐surface atmosphere during cold season especially for daily maximum temperature. However, care should be taken when using the CNRR precipitation dataset, especially for heavy rainfall cases. CNRR datasets are also able to generate high‐quality upper atmospheric products especially in CNRR‐GSI experiment, which assimilates long time series of local observations. CNRR‐GSI can better represent the lower‐level horizontal wind and temperature than ERA5. By using the three‐dimensional variational data assimilation (3D‐Var) method, CNRR‐GSI outperforms the CNRR‐SN and the driving global reanalyzes. With the increase of the computing resources, the potential opportunities for improving CNRR can be expected by applying more advanced methods.