## 3 Results
### Daily Scale Assessment  
The performance of GPPs was further evaluated at the daily scale with insight into different seasons in JRB. Table 4. shows the daily and seasonal statistics of GPPs estimates in contrast to gauges throughout the entire study period. The MSWEP showed a better correlation with gauges precipitation having a CC value of 0.86, followed by ERA-5 (0.81), APHRO (0.77), PGMFD (0.68), and CHIRPS (0.57). This correction revealed good agree  
Figure 2: Spatial variability of average annual precipitation over JRB during the entire study period 1981–2009 from **(a)** in-situ gauges, **(b)** MSWEP, **(c)** ERA-5, **(d)** APHRO, **(e)** PGMFD, and **(f)** CHIRPS. Whereas I, II, III, IV and V represent Neelum, Kunhar, Jhelum, Kanshi and Poonch sub-basins, respectively. ments between GPPs and gauges data. However, the selected GPPs underestimated daily precipitation over the JRB, with rBias values ranging from \(-5.94\%\) to \(-25.76\%\), except the ERA-5, which showed an overestimation with the rBias value of \(26.43\%\). These results are similar to other studies that have concluded similar findings of over and underestimations by ERA-5 and APHRO, PGMFD, and CHIRPS, respectively, in different regions of the world [2; 19; 26; 65; 66]. In addition, the CHIRPS exhibited high Bias values (\(-1.36\) mm/day), ensuring more systematic errors still exist. Regarding RMSE, all GPPs displayed error magnitudes ranging from \(2.29\) to \(3.99\) mm/day. All GPPs could detect the precipitation occurrence in different seasonal periods over the whole basin. The MSWEP could detect the precipitation occurrence with the highest POD value of \(0.75\) (\(75\%\)), whereas ERA-5, APHRO, PGMFD, and CHIRPS captured \(71\%\), \(61\%\), \(60\%\), and \(52\%\) of precipitation occurrence, respectively. In terms of FAR, the MSWEP showed the lowest FAR value (\(0.14\)), indicating good performance in the detection of a limited number of unrealistic precipitation events, followed by ERA-5 (\(0.15\)) and APHRO (\(0.17\)), PGMFD (\(0.19\)). The CHIRPS additionally (\(0.25\)) detected precipitation days falsely; these events are not recorded by in situ gauges. Concerning CSI, the MSWEP dataset also revealed the highest value (\(0.72\)), suggesting the best performance compared to other GPPs. A similar assessment was carried out to analyze the credibility of the GPPs in estimating seasonal precipitation. Overall, the MSWEP demonstrated good performance than other GPPs in all seasons. Precisely, it showed the higher performance in winter season with highest values of CC (\(0.90\)), POD (\(0.86\)), CSI (\(0.83\)) and lower values of RMSE (\(1.80\) mm/day), Bias (\(0.10\) mm/day), rBias (\(3.03\%\)), and FAR (\(0.06\)). In the case of ERA-5, that overestimated the observed daily precipitation amounts in entire seasons. However, it showed better performance in monsoon indicated highest values of CC (\(0.85\)), POD (\(0.81\)), CSI (\(0.78\)) and lowest values of RMSE (\(3.24\) mm/day), Bias (\(0.58\) mm/day), rBias (\(24.18\%\)), and FAR (\(0.09\)).The performance of other products such as APHRO and PGMFD was also good in all seasons, whereas APHRO performed better than PGMFD during all seasons; particularly, it exhibited the best performance in monsoon season with the highest values of CC (\(0.78\)), POD (\(0.77\)), CSI (\(0.76\)) and lowest RMSE (\(3.35\) mm/day) Bias (\(-0.33\) mm/day), rBias (\(-10.96\%\)) and FAR (\(0.10\)). While PGMFD exhibited the highest CC (\(0.71\)) and smallest values of RMSE (\(3.61\) mm/day), specifically during monsoon season, suggesting moderate agreement with the reference data. The CHIRPS product consistently indicated poor performance in seven statistical indices throughout all seasons. Based on its individual performance, it was just satisfied, particularly in monsoon with the value of CC (\(0.59\)), RMSE (\(3.86\) mm/day), Bias (\(-1.30\) mm/day), rBias (\(-23.71\%\)), POD (\(0.58\)), FAR (\(0.13\)) and CSI (\(0.57\)) compared to other seasons.