## 5 Discussion  
Previous studies have shown that satellite precipitation products do not perform well in Xinjiang, China [40]. However, in this study, we evaluated the GPM/TRMM datasets on different temporal scales (annual, seasonal, and daily) and it was found that GPM showed significant improvement compared with its predecessor TRMM. From the above analyses, it can be seen that the GPM-IMERG and TRMM datasets show obvious spatial differences in the precipitation estimation of TSM, with greater \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multicolumn{2}{c}{**POD**} & \multicolumn{2}{c}{**FAR**} & \multicolumn{2}{c}{**FBI**} & \multicolumn{2}{c}{**CSI**} \\  & **GPM** & **TRMM** & **GPM** & **TRMM** & **GPM** & **TRMM** & **GPM** & **TRMM** \\ \hline Tianshan & 0.979 & 0.836 & 0.237 & 0.201 & 1.282 & 1.046 & 0.751 & 0.690 \\ TS1 & 0.902 & 0.716 & 0.474 & 0.451 & 1.716 & 1.304 & 0.497 & 0.451 \\ TS2 & 0.952 & 0.740 & 0.381 & 0.336 & 1.537 & 1.115 & 0.600 & 0.538 \\ TS3 & 0.843 & 0.640 & 0.510 & 0.461 & 1.721 & 1.186 & 0.449 & 0.414 \\ TS4 & 0.816 & 0.730 & 0.552 & 0.485 & 1.822 & 1.417 & 0.407 & 0.433 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of precipitation detection capability. Figure 8: Probability density function (PDF) of precipitation estimation in Tianshan and four subregions. precipitation in the north than the south and in the west than the east (Figure 2). The seasonal pattern is that the precipitation is more in summer and less in winter. Both satellite products can track and observe the spatial variation of precipitation. This is consistent with other results [26]. The main reason for the formation of this feature is that the large-scale mountain of TSM intercepted and uplifted the water vapor from Atlantic and Arctic Ocean, resulting in obvious precipitation gradient. Both satellite products underestimated annual precipitation, which is reasonable and has been reported in other high mountains areas [41; 42]. Because arid areas have less rainfall and low water vapor in the atmosphere, it is difficult to detect by satellite sensors. Based on daily evaluations, it is found that compared to TMPA, GPM products have improved R, POD, and CSI values. Such improvements have also been found in other studies [18; 19]. The enhancement of these indicators of GPM products might be related to the improvements in the spatiotemporal resolution [21]. The GPM products provide precipitation estimates of 30 min intervals, while TRMM products provide estimates of 3 h periods. Due to the lower temporal resolution of TRMM products, however, some precipitation events are often missed, resulting in the lower R value. In addition, the influence of altitude on precipitation estimation for the two satellite products still exists. The R value is lower in high-elevation areas and higher in low-elevation areas. The performance of GPM/TRMM in high-elevation areas is worse than in low-elevation areas. To more comprehensively evaluate the satellite datasets, we divided TSM into four subregions according to the geographical location and climatic conditions (Figure 1) and compared the satellite datasets and ground reference dataset across different subregions. In TS1 and TS3, the R value between GPM and CGDPA is greater than that of TRMM data, while in TS2 and TS4, the R value of GPM and CGDPA is slightly smaller than that of TRMM. GPM had a lower RMSE in the four subregions and performed better (Figure 6). Regarding seasonal changes, the two satellite products had a higher correlation in summer and autumn, and a lower correlation in winter and spring. Among the four subregions, TS2 showed significant seasonality (Figure 7). The reason may be that the winter and spring seasons in the study area are affected by the Siberian air mass, the precipitation is low, the weather is dry and cold, and the mountain surface is covered with snow. It is difficult for satellite products to distinguish between snow and clouds.