## 1 Introduction  
Precipitation is a key variable of the global water cycle and the driving force for land surface hydrological processes [1; 2; 3]. Reliable precipitation input is crucial for hydrological modeling and prediction [4]. Traditionally, rainfall observations are operated through rain gauges and ground-based weather radars. However, measurements from these in situ observational networks are restricted for the remote areas and mountain regions [5]. For instance, the Tibetan Plateau (TP) with an average elevation of over 4000 m, is the highest plateau in the world [6]. To date, precipitation observation networks are sparse or nonexistent in many parts of the TP due to the complex terrain, harsh climate, and high cost [7]. Satellite precipitation products are practical means of detecting rainfall distribution over vast regions that are poorly gauged, and provide a potential complementary source beyond ground in situ data [8; 9]. Satellite precipitation products combine the advantage of the frequent sampling of infrared (IR) and more accurate precipitation estimates of passive microwave (PMW) observations and can provide quasi-global precipitation maps [10]. Since the Tropical Rainfall Measuring Mission (TRMM) started in 1997, various satellite precipitation products were made available to the public with different temporal and spatial resolutions [11; 12; 13; 14; 15]. This greatly facilitates hydrological prediction and water resource management [16; 17; 18]. However, errors and uncertainties exist in satellite precipitation products due to the indirectness of distant radiance measurements to surface precipitation, especially in high latitudes and altitudes [3; 19; 20]. For example, [PERSON] and [PERSON] [21] assessed six different TRMM-era datasets over the globe, and found high relative uncertainties over complex terrain areas, including the TP, the Rockies, and the Andes. [PERSON] et al. [22] argued that the highly accurate IR and PMW observations still have difficulties on the TP with snow cover and complex terrain. Recently, as a successor to the TRMM, the Global Precipitation Measurement (GPM) Core Observatory (GPM CO), carrying advanced precipitation sensors with the Ka/Ku-band (35.5 and 13.6 GHz, respectively) dual-frequency precipitation radar (DPR) and the multifrequency (10-183 GHz) GPM microwave imager (GMI), was launched on 28 February 2014 [23]. The GPM CO and another 10 or so satellite partners together provide the best possible sampling and coverage of precipitation observations [24; 25], and can improve the capabilities of measuring light rain and snowfall. With such a constellation of varying precipitation sensors, the Integrated Multi-satellite Retrievals for GPM (IMERG) algorithm was developed to provide a new generation of precipitation estimates around the globe with high temporal (half-hour) and spatial (0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\)) resolutions [23; 26; 27]. Based on the difference of data latency and accuracy, IMERG offers three distinct products: Early, Late, and Final Runs. These IMERG products take advantage of several existing precipitation retrieval algorithms, including TRMM Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Center Morphing with Kalman filter (CMORPH-KF), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks and a Cloud Classification System (PERSIANN-CCS), and are expected to have less error and better performance than TRMM-era satellite precipitation products. As the GPM era is now upon us, several recent studies evaluated the performance of IMERG data by comparing them against observations from gauges, radars, and other prior satellite precipitation products [10; 28; 29; 30; 31; 32]. [PERSON] et al. [33] did a preliminary comparison between the IMERG and TMPA 3B42 over coterminous United States (US) in the months April through July 2014. They reported that IMERG has good agreement with 3B42 data (correlation of 0.88). Furthermore, [PERSON] et al.