## 3 Results
### Provincial Scale Performance Evaluation  
Table 3 illustrates the performance of the CUT TS, ERA-5, IMERG-7, and MSWEP2.8 monthly precipitation estimates during the study period at the provincial scale. All the datasets exhibit poor performance in the GB province, with significant overestimations. IMERG7 depicts a better and moderate correlation with the ground observation data (CC = 62), which is closely followed by MSWEP2.8 with moderate correlation (CC = 61). However, CRU TS and ERA-5 (CC = 0.54, 0.47, respectively) exhibit a very weak association with the RG data. Along with poor correlation, the higher bias values ranging from 76.68% (IMERG7) to 450.57% (ERA-5) caused negative KGE values for each dataset, as evident in the worse performance of the products in this region. Overall, IMERG7 shows better performance in this province, closely followed by MSWEP2.8; in contrast, ERA-5 exhibits worse performance based on all the statistical indicators, followed by CRU TS. In KPK province, all the products show good performance except ERA-5. Despite a good CC of 0.75 due to the higher RMSE (59.91 mm), relatively higher bias (Pbias = 34.87%), and lower KGE value (0.32), it exhibits significant deviation from the reference values. By contrast, IMERG7 outperforms, with strong correlation (CC = 0.88), relatively low RMSE (39.24 mm), reasonable bias (Pbias = 12.54%), and higher KGE value (0.65). MSWEP2.8 and CRU TS also show good agreement with the ground observations, with good correlation,  
Figure 2: Annual mean precipitation spatial distribution maps for the rain gauge observations and selected datasets. KGE, and less bias. The statistics of CRU TS are nearly the same in KPK and Punjab; however, ERA-5 significantly improves its performance in this region and reveals a better association with the ground observations than CRU TS. IMERG7 elucidates its overall superior performance (CC = 91, PBias = 13.64%, and KGE = 75) in Punjab and closely follows the reference data. Furthermore, MSWEP2.8 also exhibits a significant association with the ground observations and shows good performance. In Sindh and Balochistan provinces, IMERG7 estimates the precipitation more precisely, with significant correlation (CC = 90 for each province) and low error values (RMSE = 20.19 mm and 11.43 mm, respectively). However, based on less bias (PBias = \(-\)0.65) and high KGE (0.69), MSWEP2.8 demonstrates outperformance in Sindh province. Moreover, ERA-5 also shows good performance in both provinces; in contrast, CRU TS elucidates normal performance, with moderate correlation, higher bias, and low KGE values in both provinces. Furthermore, IMERG7 and ERA-5 show the tendency of overestimation across all the provinces, while MSWEP2.8 underestimates the precipitation in KPK, Punjab, and Sindh provinces, and CRU TS also shows the same trend in Punjab, Sindh, and Baluchistan provinces. Overall, IMERG7 exhibits superior performance, closely followed by MSWEP2.8, over the other datasets across the study region. The box plots in Figure 3 further illustrate the performance of the datasets across provinces. The correlation box plot depicts that all the products show a weak association with RG data in GB province. CRU TS shows its best agreement in KPK, followed by Punjab, Sindh, and Baluchistan, while for ERA-5, IMERG7, and MSWEP2.8, no significant variation is observed across these four provinces. The box plots for the RMSE depict that ERA-5 consistently reduces the error values from GB to Balochistan province effectively, whereas CRU TS, IMERG7, and MSWEP2.8 improve their performance in the order of KPK < Punjab < GB < Sindh < Balochistan significantly. Furthermore, according to PBias box plots, all the datasets outperform in all the regions except GB. However, CRU TS exhibits less bias in KPK, followed by Punjab, Balochistan, and Sindh provinces. ERA-5 shows good performance in Punjab, followed by Balochistan, Sindh, and KPK. IMERG7 shows less deviation from the observed values in Punjab, followed by KPK, Balochistan, and Sindh.