Merge Satellite and Gauge Daily Precipitation Data for Areas with Sparse-Gauge and Rugged Terrain: A High-Accuracy Gridded Dataset CGS01 (2000–2024) across China

Abstract. High-precision quantification of regional precipitation is the foundation of watershed water balance analysis and water resources management. However, current approaches are hindered by methodological diversity and inconsistent results, with limited accuracy, especially in gauge-sparse regions and areas with drastic topographic relief. This study proposes a dual thin-plate spline (TPS) fusion method combining satellite observations and gauge observations to develop a daily precipitation dataset named CGS01. Multi-dimensional comparisons and comprehensive accuracy evaluations are conducted against several mainstream precipitation products including CN05.1, CMFD_V1 and CHM_PRE_V2. From a methodological perspective, this paper analyzes the core difficulties and feasible solutions for regional precipitation estimation, and discusses the error sources of GPM satellite precipitation data, as well as the application potential of the proposed method in watershed water balance assessment and high-precision global precipitation quantification. The results reveal that compared with digital elevation model (DEM) and reanalysis data used as covariates in TPS method, satellite precipitation data exhibits higher reliability when serving as covariates, while corrections for systematic errors and suppression of random errors are still required. A novel systematic error correction method for GPM data is proposed. Adopting only 27 % of the gauge numbers used in previous studies, the newly developed CGS01 presents obviously superior performance to existing datasets. The national average error of annual precipitation is 2.0 %. Validated against observed runoff data, the watershed water balance analysis error is 10.9 %. Furthermore, CGS01 can reasonably reflect the controlling effects of topography and wind direction on precipitation spatial patterns in high mountain and canyon areas without in-situ observations. Compared with CHM_PRE_V2, CGS01 daily precipitation shows improvements of 14.2 %, 2.9 %, 21.3 %, and 8.2 % in the correlation coefficient, Kling-Gupta efficiency, probability of detection, and mean absolute error. The method proposed in this study has three prominent strengths: it requires no preset empirical parameters, delivers highly reproducible results, and imposes low demands on gauge station density. The CGS01 dataset shows that from 2000 to 2024 eight out of China’s ten first level watersheds showed annually wetting trends, with trends ranging from 0.5 to 8.4 mm per year, and in seven of the ten first level watersheds, the contribution ratio of daily extreme precipitation to total precipitation increased at a rate of 0.2 %–0.5 % per year. Free access to the dataset can be found at https://doi.org/10.5281/zenodo.20176004 (Yan et al., 2026).