## 4 Results and Discussion  
### Rainfall Characteristics of the TP  
Figure 2 shows the spatial distributions of three-year daily mean precipitation for CGDPA and four GPM-era precipitation products over the TP. Generally, all precipitation sets shared similar spatial distribution: the mean precipitation decreased from the southeast to the northwest. One can see that a large amount of precipitation (more than 5 mm/day) concentrates over the southern region of the TP. The reason for this is the effect of the Himalayas, which intercept the moisture from the Indian Ocean monsoon and induce rainfall. In contrast, less precipitation is observed in most of the west and north, where the Westerlies do not prevail and the Indian monsoon is relatively weaker [68]. Compared with the CGDPA, a pronounced difference can be found between the IMERG and GSMaP precipitation products. IMERG-UC significantly underestimated the TP's precipitation and the GSMaP-MVK clearly overestimated the precipitation. However, the precipitation estimates of both gauge-adjusted products were more consistent with the ground measurements than the satellite-only products. After gauge correction, the under- and overestimations of satellite-only products were mitigated, especially in \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Statistic Index** & **Formula** & **Unit** & **Perfect Value** \\ \hline Correlation coefficient (CC) & CC & \(\frac{\sum\limits_{i=1}^{n}(G_{i}-\overline{G})^{2}(S_{i}-\overline{S})}{\sqrt {\sum\limits_{i=1}^{n}(G_{i}-\overline{G})^{2}}\cdot\sqrt{\sum\limits_{i=1}^{n }(S_{i}-\overline{S})^{2}}}\) & - & 1 \\ Root-mean-squared error (RMSE) & RMSE & mm & 0 \\ Mean error (ME) & ME & \(\frac{1}{n}\sum\limits_{i=1}^{n}\big{(}S_{i}-G_{i}\big{)}\) & mm & 0 \\ Relative root-mean-squared error (RRMSE) & RRMSE & \(\frac{\sum\limits_{i=1}^{n}(S_{i}-G_{i})^{2}}{\frac{1}{\sum\limits_{i=1}^{n}G _{i}}}\) & \% & 0 \\ Relative bias (RB) & RB & \(\frac{\sum\limits_{i=1}^{n}(S_{i}-G_{i})}{\sum\limits_{i=1}^{n}G_{i}}\) & \% & 0 \\ Probability of detection (POD) & POD & \(\frac{1}{H+M}\) & - & 1 \\ False alarm ratio (FAR) & FAR & \(\frac{F}{H+F}\) & - & 0 \\ Frequency bias index (FBI) & FBI & \(\frac{H+F}{H+M}\) & - & 1 \\ Equitable Threat Score (ETS) & ETS & \(\frac{H-H}{H+M+F-H_{i}}\) & - & 1 \\ \(H_{e}=\frac{(H+M)(H+F)}{\sum\limits_{i=1}^{n}(Q_{obs}-Q_{obs})^{2}}\) & NSE & \(-1-\frac{\sum\limits_{i=1}^{n}(Q_{obs}-Q_{obs})^{2}}{\sum\limits_{i=1}^{n}(Q_{ obs}-Q_{obs})^{2}}\) & - & 1 \\ \hline \hline \end{tabular} Notation: \(n\), number of samples; hit (\(H\), observed rain correctly detected); miss (\(M\), observed rain not detected); false (\(F\), rain detected but not observed); null (\(N\), no rain observed nor detected); \(S_{i}\), satellite precipitation; \(G_{i}\), gauged observation; \(Q_{obs}\), observed streamflow; \(Q_{obs}\), simulated streamflow; \(Q_{obs}\), mean value of observed streamflow.  
\end{table} 
Table 2: List of the validation statistical metrics for evaluating satellite precipitation products. the eastern TP with more rain gauges. Therefore, the strategy of gauge adjustments using in situ measurements greatly improved the product accuracy. Next, we displayed the rain rate distribution of daily precipitation amount and the daily number of precipitation events over the TP. As shown in Figure 3, for the intensity distribution from the CGDPA, the distribution of the precipitating amount over the TP presents a single-peak pattern (approximately at a rain rate of 14 mm/d), and the most precipitating days occurred in the precipitation intensity across the range of 2-8 mm\(\cdot\)day\({}^{-1}\). The satellite precipitation products exhibit similar distribution patterns with CGDPA in terms of precipitation rates for both the occurrence and the volume of precipitation, but there are some differences in the intensity distribution curves. For example, at the light rain range (0.25-2 mm\(\cdot\)day\({}^{-1}\)), all the satellite datasets detected more precipitation events than the reference datasets (Figure 3b).