As a major component of water cycle, the accuracy quantification of different precipitation products is critical for evaluating the climate change and ecosystem functions. However, lack of evidence is available to choose a precise precipitation product in relative applications. Here, to solve this limit, we analyze spatiotemporal pattern and accuracy of four precipitation products, including CHIRPS V2.0, PERSIANN-CDR, ECMWF ERA5-Land, and GLDAS_NOAH025_3H, over the China during the period of 1981-2020 based on the five precipitation traits (i.e., spatial pattern of multiyear average, annual trend, seasonality, frequency, and intensity), and meteorological gauge observations are taken as the benchmark. Our results show that, compared to other products, CHIRPS data has the strongest ability to present spatial pattern of multiyear average precipitation, especially in most part of northeastern and southern China. All four precipitation products can accurately depict the spatial pattern of seasonality, but poorly describe spatial pattern of precipitation intensity and frequency at a daily scale. These products only correctly predict the interannual precipitation trend in some local areas. Our findings provide evidences to select high-quality precipitation data, and could help to improve the accuracy of relative geophysical models.
 

Precipitation products; trend analysis; seasonal index; frequency and intensity; accuracy evaluation