Abstract. High-quality, freely accessible, long-term precipitation estimates with fine spatiotemporal resolution play essential roles in hydrologic, climatic, and numerical modeling applications. However, the existing daily gridded precipitation datasets over China are either constructed with insufficient gauge observations or neglect topographic effects and boundary effects on interpolation. Using daily observations from 2839 gauges located across China and nearby regions from 1961 to the present, this study compared eight different interpolation schemes that adjusted the climatology based on a monthly precipitation constraint and topographic characteristic correction, using an algorithm that combined the daily climatology field with a precipitation ratio field. Results from these eight interpolation schemes were validated using 45 992 high-density daily gauge observations from 2015 to 2019 across China. Of these eight schemes, the one with the best performance merges the Parameter-elevation Regression on Independent Slopes Model (PRISM) in the daily climatology field and interpolates station observations into the ratio field using an inverse-distance weighting method. This scheme had median values of 0. 78 for the correlation coefficient, 8. 8 mm d−1 for the root-mean-square deviation, and 0. 69 for the Kling–Gupta efficiency for comparisons between the 45 992 high-density gauge observations and the best interpolation scheme for the 0. 1∘ latitude × longitude grid cells from 2015 to 2019. This scheme had the best overall performance, as it fully considers topographic effects in the daily climatology field and it balances local data fidelity and global fitting smoothness in the interpolation of the precipitation ratio field. Therefore, this scheme was used to construct a new long-term, gauge-based gridded precipitation dataset for the Chinese mainland (called CHMPRE, as a member of the China Hydro-Meteorology dataset) with spatial resolutions of 0. 5, 0. 25, and 0. 1∘ from 1961 to the present. This precipitation dataset is expected to facilitate the advancement of drought monitoring, flood forecasting, and hydrological modeling. Free access to the dataset can be found at https: //doi. org/10. 6084/m9. figshare. 21432123. v4 (Han and Miao, 2022).
Han et al. (2023) studied this question.