Atmospheric reanalysis is a process that utilizes numerical weather prediction (NWP) models and data assimilation systems to integrate historical meteorological observations, thereby reconstructing datasets that represent past atmospheric states. This paper presents the China Meteorological Administration’s (CMA) latest global atmospheric reanalysis product, CRA1.5, which employs the Global Spectral Model (GSM) and the Gridpoint Statistical Interpolation (GSI) Data Assimilation system. CRA1.5 features a model spectral resolution of T_L1534 (equivalent to approximately 13 km in horizontal grid spacing), a product time interval of 1 h, a model top at 0.27 hPa, and a hybrid-4DEnVar scheme along with an Ensemble Kalman Filter for data assimilation. The temporal coverage of CRA1.5 extends from 1979 to near real-time. During the reanalysis production, we assimilated a substantial volume of reprocessed satellite data and extensive conventional observations. These observations include dense networks of conventional observations within China (comprising approximately 120 sounding stations and 2400 meteorological stations) as well as satellite observations from China’s Fengyun, Haiyang, and Yunyao satellite series. Compared to CRA-40, the root-mean-square error (RMSE) of geopotential height at 500 hPa is reduced by 22.46%, the RMSE of temperature at 200 hPa is reduced by 21.7%, and the RMSE of zonal wind at 850 hPa is reduced by 14.41%. CRA1.5 outperforms ERA5 in 100-m wind speed over China (RMSE: 3.21–3.36 m s⁻¹ vs. 3.46–3.59 m s⁻¹) and achieves comparable accuracy to ERA5 in atmospheric precipitable water (RMSE: 3.10 mm vs. 3.13 mm). CRA1.5’s 2-m temperature trend aligns closely with HadCRUT5, capturing global warming accurately. With higher spatiotemporal resolution (0.1° × 0.1°, hourly) and advanced assimilation, CRA1.5 provides a critical dataset for climate monitoring, NWP, and AI-based meteorological research. CRA1.5 has been implemented for real-time operation at the CMA. Updates are available up to 6 h after the analysis time, with a final update provided at a 3-day lag.

A high-quality, long-series, real-time updatable snow depth dataset of in-situ observations collected by National Meteorological Information Centre is developed. It is crucial for evaluating snow depth model and satellite remote sensing products. A comprehensive quality control procedure including the check of metadata, limit values, temporal consistency, temperature-snow depth consistency, precipitation-weather phenomenon-temperature-snow depth consistency, and spatial consistency has been applied to snow depth in-situ observations to identify erroneous data. About 0.2% erroneous data are identified, especially the false “0” value and the erroneous extremes at 148 stations, and the quality of the dataset is ensured. The dataset comprises of snow depth observations from about 2400 sites spanning from 1951 to 2023, with data completeness ranging from 80%—90% at each station. Based on this dataset, the present study investigates spatial distribution of snow in different seasons, the three major snowpack zones, extreme values, and climatic trend of mean daily snow depth and the number of snow-cover days. The findings highlight the largest snow depth and the number of snow-cover days in specific regions like Northeast China, the eastern Inner Mongolia, northern Xinjiang, and the Qingzang plateau (15—20 cm and over 80 d in winter). Spatial variability is also observed on the Qingzang plateau. Significant increasing trends are observed in Northeast China, the eastern Inner Mongolia, and northern Xinjiang, while decreasing trends are observed in the North China plain and the Qingzang plateau.