## 3 Results
### Elevation-Based Performance Evaluation of GPPs  
In this section, an elevation-based comparison is performed to further explore the skills and weaknesses of the GPPs. Table 5 presents the level of agreement of each dataset with the RGs in each region. The statistics show that IMERG7 once again outperforms in all the aspects, with higher correlation, lower RMSE, and a higher KGE value. However, the PBias value for IMERG7 is relatively higher than all the products in regions III and IV. Secondly, MSWEP2.8 shows superior performance over the other two products in regions I and II based on the higher CC and lower RMSE, and in region III based on the CC, RMSE, PBias,  
Figure 8: PBias boxplots (**a–e**) derived for selected datasets for the monthly, winter, pre-monsoon, monsoon, and post-monsoon seasons across Pakistan, respectively. The orange line in the box plot shows the standard score for each measure. and KGE. In region IV, the higher correlation, lower error value, and higher KGE make it dominant over the CRU-TS and ERA-5 datasets. ERA-5 exhibits weak agreement with the ground observations in regions I and II, with low correlation, significant errors, and higher bias in precipitation prediction. However, it performs better in region III than CRU-TS based on all the statistics and in region IV based on the CC and KGE value (Table 5). The performance of each dataset at various elevations is evaluated in Figure 10. The box plots in the first row compare the correlation of the products across the predefined elevation-based regions. The results depict that CRU TS has a significant association \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Region** & **Datasets** & **Correlation** & **RMSE (mm)** & **PBias (\%)** & **KGE** \\ \hline \multirow{4}{*}{Region I} & CRU TS & 0.570 & 30.584 & 86.067 & \(-\)0.065 \\  & ERA-5 & 0.525 & 81.285 & 404.794 & \(-\)3.198 \\  & IMERG7 & 0.653 & 26.852 & 67.275 & 0.042 \\  & MSWEP2.8 & 0.633 & 27.248 & 95.477 & \(-\)0.087 \\ \hline \multirow{4}{*}{Region II} & CRU TS & 0.758 & 67.213 & \(-\)6.430 & 0.627 \\  & ERA-5 & 0.769 & 71.205 & \(-\)6.750 & 0.507 \\  & IMERG7 & 0.898 & 55.018 & \(-\)1.182 & 0.640 \\  & MSWEP2.8 & 0.817 & 59.442 & \(-\)20.910 & 0.583 \\ \hline \multirow{4}{*}{Region III} & CRU TS & 0.669 & 31.884 & \(-\)16.737 & 0.456 \\  & ERA-5 & 0.781 & 29.507 & 13.604 & 0.598 \\ \cline{1-1}  & IMERG7 & 0.895 & 20.494 & 26.418 & 0.677 \\ \cline{1-1}  & MSWEP2.8 & 0.834 & 24.777 & \(-\)2.217 & 0.673 \\ \hline \multirow{4}{*}{Region IV} & CRU TS & 0.718 & 15.736 & \(-\)4.774 & 0.561 \\ \cline{1-1}  & ERA-5 & 0.777 & 16.333 & 16.844 & 0.615 \\ \cline{1-1}  & IMERG7 & 0.914 & 10.599 & 19.636 & 0.711 \\ \cline{1-1}  & MSWEP2.8 & 0.819 & 14.107 & 7.526 & 0.690 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Elevation-based statistical comparison of selected datasets against ground observations. Figure 9: KGE boxplots (**a–e**) derived for selected datasets for the monthly, winter, pre-monsoon, monsoon, and post-monsoon seasons across Pakistan, respectively. The orange line in the box plot shows the standard score for each measure. with the ground observations in region II, followed by region IV, and poor alignment in region I based on the correlation coefficient and KGE values. According to the RMSE, the precipitation estimation errors were higher in region II, and in regions I and III, it is nearly the same error level (RMSE = 30.54 mm and 31.88 mm, respectively), while in region IV, the performance is better, with a relatively low RMSE (15.73 mm). Furthermore, CRU TS shows the intention of underestimation in all the regions except region I, where it considerably overestimates and exhibits poor performance. The rest of the regions where good precipitation estimations are observed were region IV, followed by region II, and then region III.