Abstract: Accurate and timely monitoring of spatiotemporal variations in snow is crucial for numerical weather prediction, climate change studies, and disaster weather forecasting. This study leverages multiple datasets, including the atmospheric forcing data for China Meteorological Administration (CMA) Land Surface Data Assimilation system (CLDAS v2.0), Fengyun-4 snow cover data, in-situ snow depth observations, and land surface parameters. Snow simulations of CLM (Community Land Model), Noah (the Cmomunity Noah Land Surface Model), and Noah-MP (Noah with Muti-Parameterization Options) land surface models, the "Ensemble Square Root Kalman Filter+Direct Insertion"(EnSRF+DI) snow assimilation technique, the TC (Triple Collocation) covariance-based multi-model integration, and a multi-grid variational analysis method accounting for elevation are employed in the present study to develop a multi-source merged snow analysis product over China at the spatial and temporal resolutions of 6.25 km and 1 h respectively. Evaluation based on in-situ observations demonstrates that the quality of this product is generally superior to that of international counterparts, such as GLDAS and ERA5_Land snow depth products. Compared to snow depth simulations solely from land surface models, the assimilation of Fengyun-4 satellite snow cover data reduces the Root Mean Square Error (RMSE) of snow depth estimates by 10%. The product is provided in netcdf format, with temporal coverage beginning in November 2021 and continuing in near real-time, and an approximate data volume of 45 MB/h. It includes key variables such as snow depth, snow cover fraction, and snow depth change. This product has been recognized as a high-value meteorological dataset by the CMA and has been operationally applied in weather briefings, disaster weather monitoring, and snowmelt flood risk analysis.