## 4 Results and Discussion
### Performance of the Ensemble Streamflow Forecasts
By using the GPP method, the ECMWF ESP skill is generally improved with the skill being high for 1\(\sim\)5 lead days. The ESP skill of raw NCEP is moderate for the first 2 lead days, and it is low for the other lead days. By using the GPP method, the NCEP ESP skill is significantly improved, with the skill being high for 1 lead day and moderate for 2\(\sim\)7 lead days. However, both the BMA-ECMWF and BMA-NCEP ESPs are, on average, lowly skill for the 7 lead days. These results demonstrate the effectiveness of the GPP method in improving ESP skill, which is more significant for the NCEP with worse raw performances. To further assess the EPF and ESP performances in extreme precipitation events, a severe flood event that occurred in July 2017 (Figure 7) was chosen. Therefore, the basin-averaged precipitation obtained from raw and postprocessed EPFs were examined against observed precipitation during the one-month period from 1 to 31 July 2017. Figure 9 presents the daily precipitation obtained from basin-averaged observations and raw forecasts at the first 4 lead days. It can be seen that observations show two peak events occurring on days 8 (86 mm) and 14 (65 mm), respectively. The peak occurrence time forecasted by raw ECMWF and NCEP both closely match the observed precipitation. Raw ECMWF forecasts lower magnitudes of precipitation relative to observations during the peak event. For the remaining days, raw ECMWF consistently forecasts higher magnitudes of precipitation. The differences between raw NCEP forecasts and observations are larger than those between raw ECMWF and observations. This is consistent with the results of Section 4.1, which reveal a better performance for ECMWF in precipitation forecasting. In addition, the forecasting uncertainty of both ECMWF and NCEP are larger for longer lead days, as indicated by the wider shade areas. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Lead Days**} & \multicolumn{3}{c}{**ECMWF**} & \multicolumn{3}{c}{**NCEP**} \\ \cline{2-7}  & **Raw** & **GPP** & **BMA** & **Raw** & **GPP** & **BMA** \\ \hline
1 lead day & 0.62 & 0.59 & 0.29 & 0.49 & 0.54 & 0.28 \\
2 lead day & 0.47 & 0.54 & 0.14 & 0.28 & 0.48 & 0.19 \\
3 lead day & 0.50 & 0.53 & 0.14 & 0.15 & 0.39 & 0.10 \\
4 lead day & 0.48 & 0.53 & 0.17 & 0.12 & 0.36 & 0.12 \\
5 lead day & 0.47 & 0.50 & 0.17 & 0.17 & 0.38 & 0.15 \\
6 lead day & 0.44 & 0.44 & 0.14 & 0.13 & 0.39 & 0.19 \\
7 lead day & 0.42 & 0.39 & 0.18 & 0.21 & 0.37 & 0.20 \\ Mean value & 0.49 & 0.50 & 0.18 & 0.22 & 0.41 & 0.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CRPSS values for ensemble streamflow simulated by raw and postprocessed ECMWF and NCEP at 1–7 lead days during the 2017 period. Figures 10 and 11, respectively present the ensemble hydrographs simulated using raw and postprocessed ECMWF and NCEP at the first 4 lead days over the upper Qingjiang basin; the observed streamflow is also plotted. Figure 10a1-a4 show that the peak occurrence time predicted by raw ECMWF closely matches the observed streamflow. The differences in magnitude for the first peak event between predictions and observations are \(-11.90\%\), \(1.74\%\), \(-14.78\%\), and \(-4.94\%\), at 1, 2, 3, and 4 lead days, respectively. Additionally, the differences for the second peak event are \(6.57\%\), \(22.81\%\), \(22.31\%\), and \(35.83\%\), at 1, 2, 3, and 4 lead days, respectively. The GPP-ECMWF can predict streamflow better than raw ECMWF, indicated by the reductions of both overprediction and underprediction for the two peak events at the first 3 lead days in Figure 10b1-b3. By using the GPP method, the absolute differences are lower than \(13.38\%\) and \(10.54\%\) for the first and second peak events, respectively. However, the streamflow prediction skill decreases by using the BMA method, with considerable underestimations of the two peak events demonstrated by Figure 10(c1-c4). In addition, the bias and uncertainty become larger as the number of lead days increases.