Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> Freely accessible long-term precipitation estimates with fine spatiotemporal resolution and high-quality play essential roles in hydrologic, climatic, and numerical modeling applications. However, the existing daily gridded precipitation datasets over China either are constructed with insufficient gauge observations or neglect topographic effects and boundary effects on interpolation. Using daily observations from 2,839 gauges across China and nearby regions from 1961 to the present, this study compared eight different interpolation schemes that adjust the climatology based on monthly precipitation constraint and topographic characteristic correction, using an algorithm that combines the daily climatology field with a precipitation ratio field. Results from these eight interpolation schemes are cross validated using 45,992 high-density daily gauge observations from 2015 to 2019 over 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 has a correlation coefficient of 0.78, a root-mean-square deviation of 8.8 mm/d, and a Kling-Gupta efficiency of 0.69 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. Therefore, this scheme is used to construct a new long-term gauge-based gridded precipitation dataset across mainland China (called CHM_PR, as a member of the China Hydro-Meteorology dataset) with spatial resolutions of 0.5°/0.25°/0.1°. 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 <a href="https://doi.org/10.6084/m9.figshare.21432123.v2" target="_blank" rel="noopener">https://doi.org/10.6084/m9.figshare.21432123.v2</a> (Han and Miao, 2022).
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