## 6 Conclusions  
Based on daily-scale data, this study evaluated the GPM/TRMM products over the TSM from 1 April 2014 to 31 March 2015, using the CGDPA dataset as the reference. Three main statistical indicators (CV, R and RB) were used to compare GPM with TRMM and verify their abilities for measuring annual, seasonal and daily precipitation. We also studied the PDF and the satellite products' precipitation detection abilities. The main findings are as follows:  
1. In 2014, GPM/TRMM captured the spatial variability of annual precipitation detected in the TSM area well, showing high precipitation in the TS1 and low precipitation in the TS4. The seasonal precipitation distribution is similar to that of annual precipitation. Both GPM and TRMM underestimated the annual precipitation, but GPM had a lower RMSE than TRMM. According to the seasonal analysis, both products underestimate the spatial variability of seasonal precipitation. Middle Tianshan and east Tianshan have the best estimated summer precipitation, with lower RMSE and higher R.
2. On a daily scale, GPM and TRMM obviously underestimate the larger CV values. Overall, GPM performs better than TRMM in detecting precipitation events. GPM is superior to TRMM with regard to the POD and CSI, where the POD is significantly higher. As for the FAR and FBI, TRMM is slightly better than GPM. The detection capabilities of the GPM/TRMM for precipitation events in the middle and east of Tianshan are significantly lower than in the northwest and southwest. 3. The influence of terrain on precipitation: the study found that the influence of terrain on satellite precipitation estimation still exists. The R value is lower in high-elevation area and higher in low-elevation area, indicating that more improvement should be made in high-elevation areas. 4. By comparing the PDF, GPM shows a trend of underestimating low rain rate and high rain rate events but overestimating middle rain rate events, while TRMM tends to find more events than the ground observations. Compared with TRMM, GPM's PDF values on all thresholds are similar to the CGDPA trends; therefore, the GPM dataset performs better than TRMM at capturing all types of rainfall events. 5. Performance of GPM/TRMM in subregions: In TS1 and TS3, the correlation between GPM and CGDPA is greater than that of TRMM, while in TS2 and TS4, the R value of GPM is slightly less than that of TRMM. GPM has lower RMSE in all subregions and performs better. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**The Frequency of Precipitation (\%)**} & **CGDA** & **GPM** & **TRMM** \\ \hline \multirow{3}{*}{Tianshan} & low rain rate & 18.22 & 11.84 & 28.85 \\  & middle rain rate & 53.10 & 82.24 & 71.15 \\  & high rain rate & 28.68 & 5.92 & 0.00 \\ \hline \multirow{3}{*}{TS1} & low rain rate & 30.77 & 34.15 & 47.04 \\  & middle rain rate & 56.59 & 62.46 & 52.96 \\  & high rain rate & 12.64 & 3.38 & 0.00 \\ \hline \multirow{3}{*}{TS2} & low rain rate & 30.94 & 32.62 & 47.27 \\  & middle rain rate & 54.26 & 65.54 & 52.73 \\  & high rain rate & 14.80 & 1.85 & 0.00 \\ \hline \multirow{3}{*}{TS3} & low rain rate & 37.77 & 51.74 & 69.35 \\  & middle rain rate & 47.87 & 48.26 & 30.65 \\  & high rain rate & 14.36 & 0.00 & 0.00 \\ \hline \multirow{3}{*}{TS4} & low rain rate & 35.09 & 53.87 & 72.47 \\  & middle rain rate & 47.37 & 45.56 & 27.53 \\ \cline{1-1}  & high rain rate & 17.54 & 0.57 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance of CGDPA, GPM and TRMM in TSM and its subregions. The results of this study show that GPM exhibits significant improvements compared to its predecessor, TRMM 3B42V7. It can show more spatial details and has a better application prospect.