## 3 Results and Discussion
### Comparison of Multi-Satellite and Reanalysis Precipitation Inputs  
The temporal variation of precipitation considerably affects the hydrologic process on the land surface. To systematically evaluate the quality of the eleven precipitation products (namely, TMPA-3B42V7, TMPA-3B42 RT, IMERG-cal, IMERG-uncal, ERA5, ERA-Interim, GSMaP, GSMaP-RNL, PERSIANN-CCS, and Average Rain 1 and Rain 2), they were separately compared with CGDPA data over the two river basins from 2006 to 2018 at a daily scale. Figure 4 displays scatter plots along with their corresponding statistical metrics for the calibration period (blue dots) as well as the validation period (red dots), as derived from the basin-average precipitation of the Beijing River Basin. Among all these precipitation estimates, the findings demonstrate that the best simulations were obtained from eight precipitation datasets during the whole period; these showed good agreement with CGDPA (CC  
Figure 3: Characteristics of observed daily discharge during 2006–2018: (**a**) annual mean discharge and (**b**) flow duration curve. The dashed lines in Figure a represent the regression lines. and KGE\({}^{\prime}\) values ranging from 0.84 to 0.97 and from 0.74 to 0.97) and were especially good at capturing precipitation events (CSI values ranging from 0.68 to 0.86). However, PERSIANN-CCS showed the worst BIAS, KGE\({}^{\prime}\), and CSI (Calibration/Validation: \(-57.8\%/-62.7\%\), 0.27/0.22, and 0.49/0.50), followed by GSMaP-RNL (\(-32.5\%/-45.1\%\), 0.54/0.43, and 0.66/0.63) and ERA-Interim (\(-13.1\%/9.24\%\), 0.70/0.57 and 0.72/0.70), which was due to the fact that instrumental and systematic biases could contribute to significant uncertainties in rainfall amounts. Generally, all these corrected precipitation datasets (TMPA-3B42V7, IMERG-cal, ERA5, and GSMaP) outperformed their corresponding uncorrected datasets (TMPA-3B42 RT, IMERG-uncal, ERA-Interim, and GSMaP-RNL) (Figures 3 and 4). GSMaP exhibited the best performance of all precipitation data during the whole period (CC = 0.97/0.94, RMSE = 2.51/3.54 mm/d, MAE = 1.16/1.55 mm/d, KGE\({}^{\prime}\) = 0.97/0.94, and CSI = 0.84/0.86), followed by Average Rain 1 (CC = 0.95/0.93, RMSE = 3.22/3.83 mm/d, MAE = 1.54/1.80 mm/d, KGE\({}^{\prime}\) = 0.94/0.90, and CSI = 0.81/0.83) and Average Rain 2 (CC = 0.92/0.93, RMSE = 3.91/4.26 mm/d, MAE = 1.86/2.06 mm/d, KGE\({}^{\prime}\) = 0.90/0.90, and CSI = 0.80/0.80). It can be seen clearly that both Average Rain 1 and Rain 2 showed better metrics than their primary members except GSMaP, highlighting the feasibility of using the simple arithmetic mean method for merging multiple precipitation datasets over two study basins (Figures 3 and 4). Figure 5 shows scatter plots along with their corresponding statistical metrics for the calibration period (blue dots) as well as the validation period (red dots), derived from the basin-average precipitation of the Dongjiang River Basin. Similar to the Beijing River, results indicate that eight precipitation products could capture precipitation  
Figure 4: Scatterplots and associated statistics of **(a)** 3B42V7, **(b)** 3B42 RT, **(c)** IMERG-cal, **(d)** IMERG-uncal, **(e)** ERA5, **(f)** ERA-Interim, **(g)** GSMaP, **(h)** GSMaP-RNL, **(i)** PERSIANN-CCS, **(j)** average Rain 1, and **(k)** average Rain 2 derived from the basin-average precipitation for the training (calibration, blue dots) and testing (validation, red dots) periods over the Beijing River Basin. events quite well (CSI values ranging from 0.66 to 0.86) during the calibration period and had good agreement with CGDPA (KGE\({}^{\prime}\) values ranging from 0.72 to 0.92) during the whole period. However, PERSIANN-CCS showed the worst BIAS, KGE\({}^{\prime}\), and CSI (Calibration/Validation: \(-\)52.7%/ \(-\)59.2%, 0.32/0.24, and 0.54/0.53), followed by GSMaP-RNL (\(-\)34.1%/\(-\)25.7%, 0.52/0.57, and 0.67/0.66) and ERA-Interim (\(-\)26.2%/\(-\)7.5%, 0.60/0.55, and 0.71/0.69).