## 1 Introduction  
Precipitation is a major force in global climate change and plays a vital role in hydrological and meteorological applications [1]. As a significant phenomenon in nature, precipitation has complex characteristics of spatiotemporal variations. It is one of the critical components of the global exchange of the surface material, the hydrological cycle, and disaster prevention [2; 3]. The variability of precipitation in mountainous areas directly affects local agriculture and the ecological environment [4; 5]. Moreover, the heavy precipitation events that occurred in mountainous areas frequently generate flash floods [6]. Therefore, the acquisition of reliable precipitation information in mountainous areas is of great significance to social and economic development and related scientific research [7]. Rain gauge observation could provide a moderately accurate method for a point-based precipitation measurement. However, rain gauges in mountainous regions are often scarce, irregular, and sometimes unavailable [4; 8]. Thus, in the applications that need high spatiotemporal resolution precipitation data, such as flood disaster forecasts, gauge data are regularly insufficient [9; 10]. Contrary to rain gauge precipitations, satellite remote sensing has the advantages of thoroughly scanning the entire study region and convenient access to the data [11], providing an alternate way to monitor precipitation at regional and global scales [12; 13]. In recent decades, a series of high spatiotemporal resolutions satellite precipitation products (SPPs), have been produced with the development of various space-borne and related satellite-based precipitation retrieval algorithms, such as Artificial Neural Networks (PERSIANN) [14], National Oceanic and the Atmospheric Administration/Climate Prediction Center (NOAA/CPC) morphing technique (CMORPH) [15; 16], as well as the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) [17], the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) [18], and the Integrated Multi-satellite Retrievals for (GPM) mission (IMERG) [19]. Compared to these satellite precipitation products, the TRMM 3B42V7 precipitation product performance is higher than other products, especially in estimating extreme precipitation events in several areas worldwide [20; 21]. TRMM was launched in November 1997 by the National Aeronautics and Space Administration (NASA) with the Japanese Aerospace Exploration Agency (JAXA) collaboration. The TRMM Version-7 offers quasi-global coverage (50\({}^{\circ}\) N-50\({}^{\circ}\) S) precipitation estimates at a high spatial resolution of (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)) and temporal resolution of 3 h [18]. Given the excellent successes of the TRMM, the GPM Core Observatory satellite was set in motion by NASA and JXAX as a successor of TRMM in February 2014. Compared with TRMM, the potential of GPM to detect liquid and solid precipitation is improved by carrying a space-borne dual-frequency precipitation radar [22]. Additionally, the GPM Core Observatory carrying a conical scanning multichannel microwave imager offers a wider measurement range [19]. The lately released IMERG further expands quasi-global coverage from (50\({}^{\circ}\) N-50\({}^{\circ}\) S) to (60\({}^{\circ}\) N-60\({}^{\circ}\) S) and provides precipitation estimates with a more satisfactory spatial resolution of (0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\)) and temporal resolution of 30 min [23]. Since the deliverance of IMERG products, many studies have been conducted to evaluate and compare the performance of TMPA and IMERG products regarding rain gauges observations in many regions, such as the USA [24], Brazil [25], Japan [26], China [27], South Korea [28], Malaysia [29], Pakistan [30], South America [31], Cyprus [32], Egypt [33], and Morocco [34; 35].