Do ERA5 and ERA5-land precipitation estimates outperform satellite-based precipitation products? A comprehensive comparison between state-of-the-art model-based and satellite-based precipitation products over mainland China

Accurate precipitation retrievals with fine spatio-temporal resolutions are critical in global and regional analyses. As an important alternative of satellite-based precipitation products, model-based precipitation estimates have undergone rapid development over the past few decades. With the recent public release of the fifth generation of atmospheric reanalysis by the European Centre for Medium Range Weather Forecasts (ERA5) and ERA5-Land, it is necessary to verify whether these two latest model-based precipitation products outperform satellite-based precipitation products. This study comprehensively evaluates the performances of state-of-the-art satellite-based and model-based precipitation products, including the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Global Satellite Mapping of Precipitation (GSMaP), ERA5, and ERA5-Land, over mainland China from 2016 to 2019. The main findings are as follows: (1) Satellite-based products generally outperform model-based products, but the latter significantly perform better than the former over high-latitude regions and in winter. (2) ERA5 and ERA5-Land share similar spatio-temporal patterns and have their own advantages in terms of different types of metrics. (3) Satellite-based products perform best over subregions of subtropical and tropical monsoon climate (ST), whereas model-based products show highest performance over subregions of temperate monsoon climate (TM) and temperate continental climate (TC); both types of products show the poorest performance over subregions of plateau mountain climate (PM). (4) IMERG-Final performs best in terms of precipitation events, while GSMaP-Gauge tends to overestimate the duration of precipitation events, and model-based products tend to underestimate the mean precipitation rate of the events. These findings provide valuable insights into the error characteristics of state-of-the-art model-based and satellite-based precipitation products in the recent years, and will also serve as useful reference for the potential improvement of precipitation retrieval algorithms in the next generation.