## 1 Introduction  
Precipitation is a fundamental component of the global water cycle. It provides important information for climate research, water resource management and many other applications, and plays an important role in the interaction between the hydrosphere, atmosphere, and biosphere [1]. Due to the great spatial and temporal variability of precipitation, accurate precipitation measurements remain challenging, especially in regions with complex topography [2; 3]. Three methods have been commonly used to measure precipitation: rain gauges, weather radars and satellite sensor. A rain gauge measures precipitation directly and is considered to be the most accurate method of determining rainfall; however, as the ground stations are sparsely distributed, rain gauges have accuracy limitations because they measure rainfall at a single point, which is insufficient to accurately describe the spatial variability of precipitation [4]. The emergence of multisatellite inversion technology provides a new method for rapidly capturing precipitation information. Satellite data are the most effective means of precipitation observation on a global scale [5]. Because precipitation observation satellites generally have the characteristics of real-time, average spatial distribution, wide coverage, and high temporal and spatial resolution, they have gradually been applied to various meteorological and hydrological research [6; 7]. Among them, the Tropical Rainfall Measuring Mission (TRMM) has been widely used due to its relatively high accuracy [8; 9; 10; 11; 12; 13]. As the successor of TRMM, the Global Precipitation Measurement (GPM) Mission initiated by NASA and JAXA was launched in April 2014. It can explore the characteristics of global precipitation in a more detailed and advanced way [14; 15]. In recent years, numerous studies have been conducted worldwide to evaluate the performance of GPM. For example, [PERSON] et al. [5] used the humid Mishui basin in the mid-latitude of southern China as the research area to evaluate the products of GPM-IMERG's early, late and final run and reported that the IMERG datasets are superior to TMPA. [PERSON] et al. [16] compared and assessed different versions of GPM-IMERG, providing global and regional evaluation results for GPM algorithm developers and insights for users of the global GPM product. [PERSON] et al. [17] verified the performance of GPM over the United Arab Emirates (UAE) based on observation data from surface rain gauges. They believed that the final-run-product of GPM-IMERG not only better reflected the rainfall variability and patterns in UAE but also had potential uses for complementing or replacing the ground rainfall measurements. [PERSON] et al. [18] quantitatively evaluated the accuracy of GPM and TMPA over the Yangtze River basin, indicating that both IMERGY5 and 3B42V7 overestimated precipitation in low-elevation areas. A few studies exist regarding the product's ability to detect and estimate precipitation and extreme precipitation. [PERSON] et al. [3] compared 17-year TMPA data with GPM-IMERG data to study these products' abilities to detect and estimate heavy rainfall across India. It was concluded that the quality of GPM research product for detecting the frequency of rainstorm events across India significantly improved TMPA during the southwest monsoon season. [PERSON] et al. [19] reported that the GPM and TMPA capture the spatial patterns of extreme precipitation in China. GPM is generally better than TMPA. [PERSON] et al. [4] reported that satellite products (IMERG/GSMAP) generally capture the temporal and spatial patterns of rainstorms, but both underestimate the accumulated precipitation of storms. Satellite precipitation products have greatly facilitated the estimation of precipitation at global and regional scales, especially in areas with few surface rain gauges and poor performance of other traditional methods [20]. Recently, several scholars have studied the results of satellite precipitation products from different regions around the world [1; 17; 21].