## 3 Results
### Provincial Scale Performance Evaluation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Region** & **Datasets** & **Correlation** & **RMSE (mm)** & **PBias (\%)** & **KGE** \\ \hline \multirow{4}{*}{GB} & CRU TS & 0.54 & 28.37 & 89.19 & \(-\)0.11 \\  & ERA\(-\)5 & 0.47 & 84.79 & 450.57 & \(-\)3.67 \\  & IMERG7 & 0.62 & 27.46 & 76.65 & \(-\)0.07 \\  & MSWEP2.8 & 0.61 & 26.93 & 108.03 & \(-\)0.19 \\ \hline \multirow{4}{*}{KPK} & CRU TS & 0.76 & 50.19 & 4.74 & 0.53 \\  & ERA-5 & 0.75 & 59.91 & 34.87 & 0.32 \\  & IMERG7 & 0.88 & 39.24 & 12.54 & 0.65 \\  & MSWEP2.8 & 0.80 & 43.51 & \(-\)8.24 & 0.57 \\ \hline \multirow{4}{*}{Punjab} & CRU TS & 0.74 & 41.22 & \(-\)4.92 & 0.59 \\  & ERA-5 & 0.77 & 42.18 & 7.38 & 0.63 \\  & IMERG7 & 0.91 & 28.34 & 13.64 & 0.75 \\  & MSWEP2.8 & 0.84 & 37.18 & \(-\)9.15 & 0.64 \\ \hline \multirow{4}{*}{Sindh} & CRU TS & 0.65 & 32.58 & \(-\)20.57 & 0.42 \\  & ERA-5 & 0.80 & 27.10 & 14.92 & 0.60 \\  & IMERG7 & 0.90 & 20.19 & 30.22 & 0.63 \\  & MSWEP2.8 & 0.85 & 22.89 & \(-\)0.65 & 0.69 \\ \hline \multirow{4}{*}{Balochistan} & CRU TS & 0.66 & 17.52 & \(-\)11.96 & 0.48 \\  & ERA-5 & 0.76 & 16.54 & 8.17 & 0.60 \\ \cline{1-1}  & IMERG7 & 0.90 & 11.43 & 20.76 & 0.71 \\ \cline{1-1}  & MSWEP2.8 & 0.81 & 14.57 & 5.25 & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Provincial-based statistical compression of selected datasets against the observed values at a monthly scale. The bias between MSWEP2.8 and the observed data is near to zero in Sindh province, followed by Balochistan, KPK, and Punjab provinces. The KGE compresses the correlation, bias, and level of variability and summarizes the overall precipitation prediction potential of the datasets, where the box plot for the KGE reveals the poor performance of all the datasets, with negative values in GB province. However, CRU TS improves the precipitation estimation skills the in order of GB < Sindh < Balochistan < KPK < Punjab. ERA-5 shows nearly the same performance in Punjab, Sindh, and Balochistan; however, in KPK, the mean value of the KGE is relatively low, which shows the poor performance of ERA-5 in this province. IMERG7 demonstrates nearly similar and good performance across all the provinces (KPK, Punjab, Sindh, and Baluchistan), with reasonable variation in the mean values; however, MSWEP2.8 improves its accuracy from moderate performance in KPK to good performance in Punjab, and relatively better and similar performance in Sindh and Balochistan provinces. Figure 3: Columns 1, 2, 3, and 4 present box plots of the correlation, RMSE, KGE, and PBias, respectively. Additionally, the first, second, third, fourth, and fifth rows represent GB, KPK, Punjab, Sindh, and Balochistan, respectively. The orange line in the box plot shows the standard score for each measure. The scatter plots in Figure 4 demonstrate the level of fitness of the GPPs' precipitation with the observed precipitation in different provinces. Figure 4a shows the association of the datasets with the RGs data in GB province. All the precipitation datasets show a weak relationship with the rain gauge monthly records in GB province comparatively, while MSWEP2.8 shows relatively good agreement (R\({}^{2}\) = 0.55). In KPK and Punjab, IMERG7 exhibits the same significant alignment with the real data (R = 0.93), while the other datasets show similar agreement, with the coefficient of determination ranging from 0.79 to 0.83 for KPK and 0.83 to 0.88 for Punjab province. In Sindh province, all the precipitation products present the most reliable association with the coefficient of determination <0.90, except CRU TS, which shows moderate alignment in Sindh and Balochistan provinces, with the same coefficient value (R\({}^{2}\) = 0.67). ERA-5, IMERG7, and MSWEP2.8 exhibit a strong relationship (R\({}^{2}\) = 0.86, 0.91, 0.88, respectively) with the observed values in Balochistan province.