## 1 Introduction  
Precipitation is a key variable in regional hydrology research, and is quite susceptible to climate change. Therefore, precipitation data with high quality is greatly important for hydrological research and applications, which mainly come from satellite inversion for the spatiotemporal continuity. Along with the development and application of remote sensing technology, the inversion of satellite-derived precipitation data plays an increasingly important role in fields such as meteorological monitoring, water resource management, and environmental protection [1, 2, 3, 4, 5]. Satellite products can provide precipitation estimates with high spatiotemporal resolution owing to their advanced algorithms and plentiful sensors and have broad application prospects [6, 7, 8]. However, there are still some concerns in regard to the accuracy and reliability of precipitation data derived from satellites, as this technology is essentially an indirect way to obtain precipitation information [1, 9, 10]. Therefore, many satellite products have been subjected to comprehensive and detailed quality assessments to verify their accuracy in estimating different precipitation indicators [11, 12, 13, 14]. Considering that precipitation data obtained from meteorological stations are regarded as the most representative of reality, validation based on station-derived observations has become one of the key methods for evaluating the performance of satellite products [9, 11, 15]. The accuracy of satellite data at different temporal and spatial scales can be directly verified via comparisons with observations obtained from meteorological stations [12; 15; 16; 17]. Existing research has shown that daily satellite-derived data are quite consistent with daily precipitation observations at the regional scale [18; 19; 20]. However, satellite products may not provide reliable estimates in areas with complex terrain, such as mountains and plateaus [5; 21; 22]. For example, assessments conducted based on station-derived observations in high-altitude regions of Asia have shown that the precipitation from satellite products was closer to observations than reanalysis products, but the former technology still had difficulties in capturing solid precipitation in winter [23]. Due to the various sensors installed on different satellites and the remote sensing inversion algorithms used, this technology widely varies in terms of its capability to characterize precipitation in different regions [24; 25; 26]. Previous studies have found that the performance of precipitation products varies greatly at different temporal and spatial scales, with the assessments of different products in the same region possibly showing significantly different results [27; 28]. Accuracy tests evaluating inversion in multiple products have revealed that almost all of them had poor inversion capabilities under weak and extremely strong precipitation in warm regions [29]. When estimating precipitation on different surfaces or at different frequencies, the systematic deviation of satellite products on land is much smaller than that over the ocean. These products are more likely to produce positive deviations under low rainfall intensity and negative deviations under high rainfall intensity on land, while generally producing negative deviations for all precipitation intensities over the ocean [26]. Current assessments of satellite precipitation products mainly consider daily time scales. The seasonal accuracy of satellite-derived precipitation estimates for tropical rainfall measuring mission has been evaluated relative to ground precipitation data using statistical methods [30]. Precipitation data obtained from six satellite products with a high spatiotemporal resolution were evaluated in the sub-Saharan region at three time scales (i.e., daily, monthly, and yearly) as well as at two spatial scales (i.e., pixel and watershed) using different quantitative and qualitative statistical indicators.