## 5 Discussion  
As shown in the box plots at the daily scale, several outliers associated with PODs (Figure 3d) are found in the three products. By reviewing the original data, we find that  
Figure 11: Model simulations during the calibration and validation periods at the daily scale (Scenario II). Figure 12: Model simulations during the calibration and validation periods at the monthly scale (Scenario II). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Indexes**} & \multicolumn{2}{c}{**Daily**} & \multicolumn{2}{c}{**Monthly**} \\ \cline{2-5}  & **Calibration Period** & **Validation Period** & **Calibration Period** & **Validation Period** \\ \hline R\({}^{2}\) & 0.65 *** & 0.64 *** & 0.85 *** & 0.85 *** \\ NSE & 0.65 & 0.43 & 0.84 & 0.84 \\ BIAS & \(-0.03\) & +0.01 & +0.06 & \(-0.02\) \\ \(p\)-factor & 0.74 & 0.69 & 0.76 & 0.75 \\ R-factor & 0.69 & 0.70 & 0.57 & 0.36 \\ \hline \hline \end{tabular}  
* **: \(p<0.001\).  
\end{table} 
Table 5: Indices for the performance of the SWAT model in calibration and validation periods (Scenario II). the outliers in the POD results come from the same station. Meanwhile, the RMSE and Bias results associated with this station are not notably different from those associated with other stations. Hence, we modestly conclude that some small precipitation events are not fully captured in this grid by the GPM. Despite this, the POD results still indicate that the IMERG products deliver better performance compared with other satellite-based precipitation products [5]. The retrieval algorithms of GPM sensors have received considerable improvement, while the Core Observation platform is equipped with the world's first dual-frequency precipitation radar (DPR; Ku band at 13.6 GHz and Ka band at 35.5 GHz) [43]. In addition, as illustrated in Figure 4, outliers can also be found in the RMSE results of FR. By checking the original data, we discover that the outliers in the RMSE results also come from the same station, and they may be mainly caused by the fact that more heavy events happened at this station, leading to larger error magnitudes and RMSEs, because RMSE tends to amplify larger errors. Actually, the values of these outliers are also within the acceptable error range, hence they can still offer valuable spatial rainfall information in the next step of runoff simulation. A large number of studies have evaluated the performance of IMERG products in China [44; 45; 46]. Scholars generally agree that satellite-based precipitation products have different accuracies in different climates and altitudes [44], while the IMERG series products show a better performance over humid regions than over high elevation zones in China [1]. Among the three IMERG products, FR always has a better accuracy than ER and LR. It is a widely held view that the FRs have better hydrological utility than ER and LR products [14; 45]. Similar to these studies [14; 45], FR is superior to ER and LR in terms of the CORR index (Table 2) in the XRRB. However, runoff simulation results driven by the FR product show higher deviations compared with those of ER and LR and most high flows are often overestimated in runoff simulations (Figure 7d, Figure 9). The reason may be that the FR after bias correction using the GPCC precipitation dataset fails to fully capture the spatial and temporal changes in precipitation in the XRRB. In a runoff simulation, the potential large errors caused by precipitation input are propagated. In Scenario II, the model after direct parameter rate-setting of FR improved the overestimation at high flows (Figures 11 and 12) and reduced the BIAS values of the FR in daily and monthly scale runoff simulations during the validation period (2016-2017) (BIAS = 0.01 and \(-\)0.02). Therefore, the accuracy of runoff simulation in Scenario II is effectively improved through recalibration of the model parameters using FR.