## 4 Results and Discussion  
### Performance of the Ensemble Precipitation Forecasts  
Figure 3 presents the comparison of daily observed precipitation and raw EPFs from ECMWF (a1-a2) and NCEP (b1-b2) at 1 lead day during 2017. The left subplots are the time series of forecast and observed precipitation, and the right subplots are the scatter plots of ensemble mean forecast and observed precipitation. The time series of forecast and observed precipitation show that there are substantial biases between raw EPFs and observations. The scatter plots show significant underestimations of peak events by raw EPFs from both ECMWF and NCEP, which is consistent with the findings of previous studies [25; 42; 43]. [PERSON], [PERSON] [42] found that heavy precipitation was generally underestimated over all of China, especially in the western region of China and South China due to the resolution and the related parameterization of convection. The underestimation of heavy precipitation is a concern of hydrological forecasting centers predicting discharge peaks and timing, as indicated by [PERSON], [PERSON] [44]. Figure 4 presents the MAE and CRPSS of raw and postprocessed EPFs at all 7 lead days. Figure 4(a1,a2) are for ECMWF, and (b1-b2) are for NCEP. It can be seen that: (1) The MAE of raw ECMWF and NCEP are about 2.40-3.92 and 2.86-6.20 at all 7 lead days, respectively. The CRPSS of raw ECMWF and NCEP are about 0.49-0.65, 0.12-0.56 at all 7 lead days, respectively. The smaller MAE and larger CRPSS of ECMWF reveal better performance than NCEP. (2) The MAE/CRPSS values of the ensemble precipitation forecasts show a clear increase/decrease with the increase in the lead days, indicating a significant downward trend of the performance. (3) Figure 4(a1,a2) show that there are no significant improvements for ECMWF after postprocessing in terms of MAE and CRPSS. The CRPSS value of BMA-ECMWF is even slightly smaller than that of raw ECMWF. (4) Figure 4(b1,b2) show that both two postprocessing methods gain in skill compared to raw NCEP. Specifically, the MAE of GPP-NCEP decreases by 0.35-1.94 mm for 1-7 lead days; the MAE of BMA-NCEP decreases by 0.36-2.02 mm for 1-7 lead days; the CRPSS of GPP-NCEP increases by 0.09-0.34 for 1-7 lead days; the CRPSS of BMA-NCEP increases by 0.02-0.31 for 1-7 lead days. Moreover, the MAE and CRPSS ranges of postprocessed EPFs are both narrower than those of raw EPFs. These positive effects of the postprocessing methods for raw NCEP proved its necessity. Generally, the results above demonstrate that  
the GPP method performs better than the BMA method in improving forecasting skill and indicate the effectiveness of the postprocessing method for NCEP with a relatively inferior raw performance. Figure 5 presents the monthly variation in MAE and CRPSS of raw and postprocessed EPFs at 1 lead day. Figure 5(a1,b1) show evident monthly variation in MAE of both raw and postprocessed EPFs, where there are larger values in June-July and smaller values in winter. There is little difference in MAE values between postprocessed and raw EPFs. Figure 5(a2,b2) also show evident monthly variation in CRPSS for raw EPFs, where the values in winter are much larger than those in other months, and the difference is up to 0.6, while the monthly variation in CRPSS for postprocessed EPFs are no longer larger, which is less than 0.23 and 0.18 for ECMWF and NCEP, respectively. Figure 3: Comparison of daily observed precipitation and raw EPFs from ECMWF (**a1,a2**) and NCEP (**b1,b2**) at 1 lead day during 2017. (**a1,b1**) are the time series of forecast and observed precipitation; (**a2,b2**) are the scatter plots of ensemble mean forecast and observed precipitation.