## 4 Results and Discussion
### Statistical Performance of Satellite Precipitation Estimates  
In this section, we evaluated the satellite precipitation products against gridded gauge-based precipitation products over the period from April 2014 to March 2017. In order to ensure a more accurate comparison, only grid pixels with at least one gauge (132 grids) were taken to calculate the statistical metrics. Figure 4 shows the scatterplots of daily IMERG-UC, IMERG-C, GSMaP-MVK, and GSMaP-Gauge data versus CGDPA for the selected grids. The evaluation metrics are also given in the figure. For the contingency table statistics (i.e., POD, FAR, FB, and ETS), a common threshold of 1.0 mm\(\cdot\)day\({}^{-1}\) was used to determine the rain/no rain event, as suggested by many previous studies [7; 69; 70; 71]. Generally, among the four satellite precipitation products, GSMaP-Gauge exhibited the best performance with the highest CC of 0.77, while GSMaP-MVK had the worst performance with a poor CC of 0.52. The IMERG-UC and IMERG-C products showed middle performance, with CC values of 0.67 and 0.70, respectively. For the satellite-only products, we can see that IMERG-UC significantly underestimated the precipitation by about \(-\)39.32%, and GSMaP-MVK overestimated the precipitation with an RB value of 26.11%. After the gauge calibration, the RB had a downward trend and the scatter points were clustered more closely to the 1:1 line than those of satellite-only estimates. Consequently, both gauge-adjusted products only showed slight underestimation relative to reference observations (Figure 4b,d). In terms of the contingency table statistics, when compared to satellite-only products, the gauge-adjusted products also had better performance (with higher POD and ETS values). The gauge-adjusted products had more detected events compared to satellite-only products. However, it is worth noting that the FAR increased from IMERG-UC to IMERG-C. We argue that the calibration scheme of IMERG resulted in an increase in the number of false events over the TP. Thus, it contributed to the observed increases in FAR values. Correspondingly, the RMSE and RRMSE of IMERG-C did not improve over that of IMERG-UC. Figure 3: Intensity distribution of (**a**) daily precipitation amount (mm\(\cdot\)day\({}^{-1}\)) and (**b**) precipitation events (count per day) from the four satellite precipitation estimates at the TP. The logarithmic scale was used to bin the precipitation rates. To investigate the spatial distributions of error metrics, CC, RMSE, and RB were computed from the four satellite precipitation products, as shown in Figure 5. In general, the CC values of all products were good over most regions of TP. Spatially, higher CC values are observed in the east TP compared to the west. This pattern of CC is attributed to the limitations of retrieval of satellite precipitation in mountainous and high-elevation regions [19; 66]. We also note that the GSMaP-Gauge showed best correspondence with gauge measurements, with larger correlation and smaller error (Figure 5j-l), which is consistent with the above statistical results. Interestingly, the spatial distributions of error metrics could explain the phenomenon of the RMSE of IMERG-UC being lower than that of GSMaP-MVK, but the bias performance of IMERG-UC was worse than that of GSMaP-MVK. The reason is that the positive and negative biases could cancel each other out. As shown in Figure 5c, IMERG-UC underestimated reference precipitation over almost all of TP, while GSMaP-MVK showed overestimation in the east and underestimation in the west (Figure 5i). Thus, for GSMaP-MVK, the magnitude of the total RB was reduced. However, focusing on the spatial distribution of RMSE, we can see that GSMaP-MVK still demonstrated the largest error among the four satellite precipitation products. Figure 4: Scatterplots of daily precipitation for **(a)** IMERG-UC; **(b)** IMERG-C; **(c)** GSMaP-MVK; **(d)** and GSMaP-Gauge against CGDPA from the 132 selected 0.25\({}^{\circ}\) grid boxes over the TP.