## 3 Results
### Daily Scale Assessment
Overall, the CHIRPS product's performance was poor and can be categorized as a poor-performing dataset among all GPPs in the JRB. In contrast to all GPPs, the MSWEP exhibited good performance in detecting precipitation incidence, as substantiated by significant values of CSI ranging from \(0.72\) to \(0.83\). As a way to determine which dataset gives more precisely precipitation estimations over the JRB, thus, we conspired Taylor diagrams [67] to assess the degree of closeness between GPPs and gauge data about CC, RMSE, and standard deviation SD (SD shows the deviation of data from its mean value) at different time scales (Figure 3). In the Taylor diagrams, the angular coordinates (congress blue dashed lines) represent the CC values, and the RMSE values are illustrated by the concentric semi-circles (green color). In comparison, the radical coordinates (black dashed lines) are the standard deviation. In Taylor diagrams, the dataset is considered a better product when the point of GPPs is closer to the reference point. Based on Figure 3, the MSWEP performed better than other products throughout all periods. However, other products showed moderate performance in RMSE and SD during all time scales, except CHIRPS, which exhibits comparatively poor performance in terms of both RMSE and SD. In comparison, the MSWEP illustrates the lowest values of RMSE and SD at all timeframes. Figure 4 depicts the spatial distribution of evaluated statistical metrics such as CC, Bias, and RMSE of selected GPPs with reference data during the entire period of daily precipitation over different parts of the JRB (Indo-Pak). Generally, the estimates from MSWEP showed the highest CC values and smaller values of Bias and RMSE across the basin (at lower and higher altitudes), suggesting a strong similarity with gauges data. In contrast, the CHIRPS exhibited the lowest degree of agreement (higher Bias and RMSE values), specifically in the western part of the basin from mid to high altitudes. However, some moderate CC values are observed in the center, stretching to the southeastern part of the basin at lower and middle latitudes. In addition to MSWEP, the ERA-5, APHRO, and PGMFD all display higher and lower correlation values in the western and eastern zones, respectively (Figure 4a). The selected GPPs showed Biases over the highlands of JRB, mainly in the western parts (Figure 4b). The overestimations by ERA-5 are owing to erroneous numerical models and observations in the data integration system [68]. However, one of the plausible explanations for the underestimation findings of GPPs (except ERA-5) is high evaporation rates. Thus, liquid water in the atmospheric profile doesn't entirely fall as precipitation in this region [69]. Many researchers have also reported significant rates of evaporation in the highlands of Hindukush Karakurm and the Himalayas [25; 70; 71]. The GPPs estimates showed fewer Biases at lower altitudes (in the western region) of the basin. In general, MSWEP displayed substantial signs of progress in estimating the precipitation occurrences than the other four products. The findings divulged that the newly developed precipitation dataset (MSWEP) improved all statistical evolution indices across the JRB more than other GPPs.