## 3 Results
### Temporal Performance Evaluation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time Series** & **Datasets** & **Correlation** & **RMSE (mm)** & **PBias (\%)** & **KGE** \\ \hline \multirow{4}{*}{Monthly} & CRU TS & 0.66 & 32.71 & 2.62 & 0.40 \\  & ERA-5 & 0.73 & 39.35 & 75.82 & \(-\)0.03 \\  & IMERG7 & 0.86 & 23.35 & 29.55 & 0.57 \\  & MSWEP2.8 & 0.79 & 26.91 & 13.30 & 0.54 \\ \hline \multirow{4}{*}{Winter} & CRU & 0.63 & 52.59 & 14.42 & 0.42 \\  & ERA-5 & 0.77 & 87.42 & 82.82 & \(-\)0.16 \\  & IMERG7 & 0.80 & 45.13 & 35.91 & 0.45 \\  & MSWEP2.8 & 0.78 & 42.51 & 23.68 & 0.44 \\ \hline \multirow{4}{*}{Pre-Monsoon} & CRU & 0.42 & 48.55 & \(-\)3.52 & 0.07 \\  & ERA-5 & 0.57 & 67.62 & 45.36 & \(-\)0.01 \\  & IMERG7 & 0.78 & 36.88 & 35.24 & 0.44 \\  & MSWEP2.8 & 0.61 & 42.72 & 9.90 & 0.34 \\ \hline \multirow{4}{*}{Monsoon} & CRU & 0.57 & 93.02 & 15.45 & 0.16 \\  & ERA-5 & 0.65 & 127.50 & 115.33 & \(-\)0.54 \\  & IMERG7 & 0.83 & 70.23 & 49.94 & 0.27 \\  & MSWEP2.8 & 0.72 & 79.18 & 21.15 & 0.33 \\ \hline \multirow{4}{*}{Post-Monsoon} & CRU & 0.54 & 22.76 & \(-\)0.11 & \(-\)0.04 \\  & ERA-5 & 0.75 & 35.91 & 193.64 & \(-\)1.39 \\ \cline{1-1}  & IMERG7 & 0.90 & 17.13 & 57.94 & 0.09 \\ \cline{1-1}  & MSWEP2.8 & 0.82 & 21.10 & 85.112 & \(-\)0.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Temporal scale statistical comparison of selected datasets against ground observations. mance. CRU TS and ERA-5 present nearly similar poor performance during the winter and pre-monsoon seasons due to the significant gaps between the predicted precipitation and the RG-based precipitation. Figure 6: Correlation 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. Figure 7: RMSE boxplots (**a–e**) derived for selected datasets for the monthly, winter, pre-monsoon, monsoon, post-monsoon and seasons across Pakistan, respectively. The orange line in the box plot shows the standard score for each measure. Figure 8 evaluates the PBias of the global datasets as various time scales against the observed data. There is significant variation observed among the datasets for overestimations, underestimations, and unbiased precipitation simulation. CRU TS shows the best performance during the post-monsoon, followed by the monthly precipitation, pre-monsoon, winter, and monsoon. Overall, CRU TS significantly outperforms for all the time scales based on the bias level. ERA-5 consistently shows worse performance with higher levels of overestimations. IMERG7 exhibits its best performance with fewer overestimates for the monthly time scale, followed by winter and pre-monsoon (PBias = 35 for each dataset), and monsoon season. During the post-monsoon, it shows worse performance, with higher deviation from the gauge data. Moreover, MSWEP2.8 shows good performance for the pre-monsoon season, with low deviation, followed by the monthly, monsoon, pre-monsoon, and post-monsoon seasons. An overall summary of the correlation, PBias, and variability is expressed by the KGE value in Figure 9. EAR-5 shows worse performance for all the timescales, with negative KGE values, while the other products also perform poorly during the post-monsoon. IMERG7 and MSWEP2.8 exhibit their best performance for the monthly time scale precipitation estimates, followed by winter, pre-monsoon, and monsoon, respectively. CRU TS shows good agreement with the observed values for the winter season, followed by the monthly scale and monsoon. Overall, IMERG7 exhibits a superior association with the rain gauge data for all the time scales except the winter season, where MSWEP2.8 shows relatively good agreement.