## 6 Discussion  
### General Remarks  
The increasing availability of GPPs has attracted the interest of many scholars in a wide range of academics. Accordingly, the developed GPPs have been widely employed for different purposes worldwide [20; 23; 29; 38; 46; 81; 88]. However, the lack of quantitative confidence in GPPs remained a critical concern that should be addressed appropriately [89]. This is due to the fact that any existing error in precipitation estimates, such as magnitude errors, will propagate into the used models and final results, in which precipitation estimates are incorporated [90; 91; 92]. For instance, studies reported the impact of bias error on specific parameters in hydrological modeling, such as peak flow and catchment modeling, as well as the effect of precipitation underestimation/overestimation on drought/wet event detection and drought monitoring [93; 94; 24; 95]. Additionally, the introduced error may be amplified through the modulation, causing higher uncertainties and making the \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**GPPs**} & \multicolumn{3}{c}{**Continuous Metrics**} \\ \cline{2-4}  & CC & **MBE (mm)** & **RMSE (mm)** \\ \hline TMPA & 0.706 \(\pm\) 0.171 & \(-\)0.58 \(\pm\) 0.58 & 0.63 \(\pm\) 0.59 \\ CHIRPS & 0.662 \(\pm\) 0.204 & 0.16 \(\pm\) 0.57 & 0.39 \(\pm\) 0.52 \\ PERSIANN & 0.582 \(\pm\) 0.264 & 0.09 \(\pm\) 0.58 & 0.48 \(\pm\) 0.42 \\ GSMaP & 0.571 \(\pm\) 0.238 & \(-\)0.13 \(\pm\) 0.40 & 0.40 \(\pm\) 0.37 \\ IMERG & 0.781 \(\pm\) 0.161 & 1.01 \(\pm\) 0.53 & 1.11 \(\pm\) 0.56 \\ ERA5 & 0.762 \(\pm\) 0.168 & 0.28 \(\pm\) 0.46 & 0.44 \(\pm\) 0.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Continuous metrics calculated from yearly average precipitations values of the GPPs and synoptic stations at the station level. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**GPPs**} & \multicolumn{3}{c}{**Continuous Metrics**} \\ \cline{2-4}  & CC & **MBE (mm)** & **RMSE (mm)** \\ \hline TMPA & 0.703 \(\pm\) 0.166 & \(-\)209.20 \(\pm\) 211.57 & 223.23 \(\pm\) 215.04 \\ CHIRPS & 0.660 \(\pm\) 0.187 & 59.44 \(\pm\) 208.95 & 137.78 \(\pm\) 187.35 \\ PERSIANN & 0.581 \(\pm\) 0.253 & 34.79 \(\pm\) 209.54 & 173.25 \(\pm\) 153.11 \\ GSMaP & 0.580 \(\pm\) 0.212 & \(-\)44.23 \(\pm\) 140.09 & 139.06 \(\pm\) 123.73 \\ IMERG & 0.797 \(\pm\) 0.146 & 362.66 \(\pm\) 188.97 & 389.29 \(\pm\) 183.38 \\ ERA5 & 0.733 \(\pm\) 0.139 & 97.47 \(\pm\) 164.13 & 151.28 \(\pm\) 141.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Continuous metrics calculated from yearly accumulated precipitations values of the GPPs and synoptic stations at the station level. final results less reliable [91]. Therefore, the best practice as the prerequisite is to evaluate GPPs in order to quantitate the magnitudes of errors and identify the error characteristics adequately. This will allow one to choose a product with higher accuracy and make it possible to model the error propagation for any specific goal, and thus, the final results can be provided with associated uncertainties as auxiliary information. This may result in better representations of the real-world situation when using GPPs and promoting informed decision making. Additionally, the validation results in any specific region could be considered an effective proxy to foster enhancements in GPPs globally or regionally [32]. It is essential to incorporate various metrics to evaluate the performance of GPPs from different and complementary perspectives. In this regard, continuous and categorical metrics focusing on precipitation intensity and occurrence evaluation could be used. Accordingly, comprehensive validation research should include both aspects to benefit the research community. Furthermore, a critical reason that lies under the consideration of various metrics is that no single metric could manifest the performance of GPPs, and no single perspective could satisfy the requirement of the whole research community.