## 3 Results and Discussion
### Comparison of Multi-Satellite and Reanalysis Precipitation Inputs
According to CC, RMSE, MAE, and CSI, GSMaP exhibited the best performance of all precipitation data during the whole period (CC = 0.96/0.85, RMSE = 3.56/5.61 mm/d, MAE = 1.34/1.88 mm/d, and CSI = 0.86/0.82), followed by the Average Rain 1 (CC = 0.95/0.83, RMSE = 3.72/6.16 mm/d, MAE = 1.70/2.28 mm/d, and CSI = 0.82/0.79) and Rain2 (CC = 0.93/0.82, RMSE = 4.29/6.30 mm/d, MAE = 2.06/2.59 mm/d, and CSI = 0.80/0.77). Additionally, Average Rain 1 had the highest KGE\({}^{\prime}\) (0.92/0.83) during the whole period, followed by Average Rain 2 during the calibration period or by GSMaP during the validation period. This implies that the simple averaged multi-source precipitation data could serve as a more suitable choice in climatology studies than their primary members for the Dongjiang River Basin. Figure 6 presents the comparison of sample proportions in different precipitation intensities, as obtained by the gauged observation and 11 precipitation products over the study period. It was obvious that the sample proportions of no/tiny rain and light rain levels of all precipitation were 0.80-0.94 and 0.83-0.93 over the Beijing and Dongjiang River Basins, respectively; meanwhile, heavy and storm rain were relatively rare (<0.05). For moderate and above precipitation intensities, the sample proportions of PERSIANN-CCS and GSMaP-RNL were much lower than that of CGDPA. Additionally, all the proportions  
Figure 5: 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 Dongjiang River Basin. of corrected precipitation products were slightly closer to the reference than their uncorrected ones. For the Beijing River (Figure 6a), the majority of the evaluated products could well capture the distribution of precipitation of CGDPA, but estimation biases were still observed. For instance, PERSIANN-CCS, GSMaP-RNL, TMPA-3B2V7, and 3B42 RT noticeably overestimated the proportion of no/tiny rain and underestimated that of light and moderate rains. By contrast, both PERSIANN-CCS and GSMaP-RNL showed similar precipitation frequency distributions to CGDPA, with the most common being no/tiny rain over the Dongjiang River Basin (Figure 6b). Figure 7 illustrates the spatially distributed KGE\({}^{\prime}\) of various precipitation products against CGDPA at a spatial resolution of 0.25\({}^{\circ}\) and a daily scale from 2006 to 2018 for the basins. Overall, Average Rain 1 had the highest KGE\({}^{\prime}\) (0.71-0.89), followed by GSMaP (0.48-0.95) and Average Rain 2 (0.65-0.83). As shown in the figure, all the corrected precipitation datasets (TMPA-3B42V7, IMERG-cal, ERA5, and GSMaP) showed higher KGE\({}^{\prime}\) values than their corresponding uncorrected datasets (TMPA-3B42 RT, IMERG-uncal, ERA-Interim, and GSMaP-RNL). The results confirm that PERSIANN-CCS performed the worst with approximately 90% of KGE\({}^{\prime}\) values less than 0.3; this phenomenon seems reasonable because its limited features were insufficient to interpret actual cloud types [20]. Furthermore, GSMaP-RNL and ERA-Interim (KGE\({}^{\prime}\) = 0.18-0.65 and 0.33-0.55, respectively) showed notably poorer performances than any of the others. Figure 6: Comparison of sample proportions at different precipitation intensities for CGDPA and \(11\) precipitation products over the (**a**) Beijing River and (**b**) Dongjiang River Basins. The Taylor diagrams were applied to analyze the error characteristics of precipitation datasets at a basin scale; specifically, we focused on precipitation intensity evaluation. Figure 8 shows that according to the CGDPA points (black stars), the closer the Taylor diagram points representing the multi-source precipitation products are, the better the accuracy.