Evaluation of IMERG, TMPA, ERA5, and CPC precipitation products over mainland China: Spatiotemporal patterns and extremes

A comprehensive assessment of representative satellite-retrieved (Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA)), reanalysis-based (fifth generation of atmospheric reanalysis by the European Centre for Medium Range Weather Forecasts (ERA5)), and gauge-estimated (Climate Prediction Center (CPC)) precipitation products was conducted using the data from 807 meteorological stations across mainland China from 2001 to 2017. Error statistical metrics, precipitation distribution functions, and extreme precipitation indices were used to evaluate the quality of the four precipitation products in terms of multi-timescale accuracy and extreme precipitation estimation. When the timescale increased from daily to seasonal scales, the accuracy of the four precipitation products first increased and then decreased, and all products performed best on the monthly timescale. Their accuracy ranking in descending order was CPC, IMERG, TMPA, and ERA5 on the daily timescale and IMERG, CPC, TMPA, and ERA5 on the monthly and seasonal timescales. IMERG was generally superior to its predecessor TMPA on the three timescales. ERA5 exhibited large statistical errors. CPC provided stable estimated values. For extreme precipitation estimation, the quality of IMERG was relatively consistent with that of TMPA in terms of precipitation distribution and extreme metrics, and IMERG exhibited a significant advantage in estimating moderate and heavy precipitation. In contrast, ERA5 and CPC exhibited poor performance with large systematic underestimation biases. The findings of this study provide insight into the performance of the latest IMERG product compared with the widely used TMPA, ERA5, and CPC datasets, and points to possible directions for improvement of multi-source precipitation data fusion algorithms in order to better serve hydrological applications.