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
CHM_PRE, 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).