## 3 Results
### Comparison at the Sub-Catchment Scale  
The spatial variability of five GPPs with reference data was also assessed and compared at Sub-Basins (SBs) scales. Based on the topologic relationship between the mainstream and tributaries, the five SBs were delineated (Figure 1c), where the SBs I and II to upper reach are high elevated zones, III and IV SBs are situated at a mid-elevation region associated with middle reach and SB V is located at low to mid-elevation zone related to lower reach Table 1. For the performance evaluation of five GPPs, the box diagrams were plotted to emphasize the distribution of 22 stations. Tables were created to show the attributes of seven statistical indices together with different seasons and in different SBs. Based on the daily assessment, the average CC value of MSWEP (0.61) is higher than ERA-5 (0.59), APHRO (0.58), and PGMFD (0.46); however, the highest and lowest agreement with reference data was observed in SBs V and I (at minimum and maximum elevations) respectively. Furthermore, during the winter, MSWEP, ERA-5, and APHRO underscored better correlation than daily scales, whereas PGMFD was inadequate, thus highlighting a smaller mean CC value Figure 5. It is worth mentioning that the CHIRPS continuously showed poor performance at mid to high elevation zones, particularly in SBs III, II, and I, with CC values of 0.38, 0.37, and 0.14, respectively, while it had the highest CC values of 0.45 and 0.59 in SBs IV and V (at lower elevation) respectively (Table S2). All GPPs (excluding ERA-5) underestimated precipitation with average BIAS values varying from \(-\)10.33% to \(-\)38.97%, in particular, SBs I, II, IV, and V at a daily scale, and in SBs I and II during the winter season, while overestimated with BIAS value stretching from 2.65% to 61.64% in SB III at daily scale and in SBs III, IV and V (43.97-69.95%, 8.3-37.23% and 17.76-55.81% respectively) in winter. Among all GPPs, the CHIRPS product mostly underestimated the rainfall (with slight overestimation), with average BIAS from \(-\)38.97% to 15.63%. ERA-5 was the most overestimated precipitation, with mean BIAS 30.98% to 49.39% in all seasons, with the highest (69.95%) and lowest (37.23%) in SBs III, IV, and V during the winter season. Although, it also showed underestimations with BIAS values ranging from \(-\)7.96% to \(-\)1.35% in SB II at both (Daily and winter) scales. The number of sites overestimated by ERA-5 (BIAS > 0) is higher than the rest of the GPPs on the SBs level and are entirely basin. It  
Figure 5: The performance of GPPs in SBs daily and during monsoon season. The lines of each box from bottom to top represent the minimum, first quartile, median, third quartile, and maximum values, respectively. (**a**) Correlation coefficient, (**b**) Root Mean Square Error, (**c**) relative Bias, (**d**) Relative Bias in percentage, (**e**) Probability of Detection, (**f**) False Alarm Ratio, (**g**) Critical Success Index. can be observed from the average values of RMSE that MSWEP has low errors (ranging from 2.88 mm to 4.01 mm) in contrast to other products in all SBs and seasons. Generally, the mean POD values of GPPs show the occurrence of precipitation events were correctly captured during monsoon than in other seasons, particularly at low elevated SBs like IV and V. Similarly, incorrectly estimated the occurrence of precipitation event (FAR values) by GPPs were larger (as elevation increases) in SBs III, II and I concerning V and IV (as elevation decreases). Conversely, the average detection capability of all products (CSI) is higher in V and IV than in III, II, and I in the monsoon season. As aforementioned in Section 2.1, this region is mostly influenced by monsoon precipitation during the summer season. However, detecting and measuring precipitation occurrences in various weather conditions at high altitudes is a challenge for satellites as reflectivity from snow and ice reduces the effectiveness of passive sensing in mountainous regions [22].