## 4 Discussion  
According to the evaluation findings of Section 3, the vertical performance of GPPs revealed that these products were less accurate at capturing large variations in precipitation at higher altitudes than horizontal and showed both spatial (basin to basin) and temporal (season to annual) biases (Section 3.3), which might be caused by coarser resolution and noticeable orographical influences in the highlands of JRB. Nevertheless, the inter-comparison of the five GPPs revealed that the newly launched precipitation dataset (MSWEP) performance is better (with slight underestimates) than all other products. These findings are consistent with the available performance evaluation studies [2; 11; 66; 69; 72; 1]. The CHIRPS displayed more considerable BIAS during all time scales, signifying that it could not accurately capture rainfall in the complex topographical region and exhibited a weaker correlation at daily and seasonal scales with CC values (<0.6) over the entire basin (Table 2). The results are consistent with [PERSON] and [PERSON] [73], who found a poor (CC = 0.47 nearest to 0.42) correlation in Eastern Nile Basin Burkina Faso (West Africa). However, it intimates that possibly merging those in MSWEP offers maximal improvements. The substantial bias in the estimations of precipitation amounts from ERA-5, APHRO, CHIRPS, and PGMFD are also pointed out by many researchers in different regions such as the UIB [2; 26], Adige Basin (Italy) [19], Mainland of China [74], Iran [65], and the eastern periphery of the Tibetan plateau [66]. The superior performance of MSWEP to the rest of the products could be owing to the latest development in (1) addition of the cumulative distribution function (CDF) and precipitation frequency correction for the spurious light rain, temporal discontinuities, and high peaks. (2) increment in spatial resolution to enhance the local suitability of precipitation estimations, which is significant for the high water yielding mountainous areas, and (3) implementation of precipitation estimations derived by Gridded Satellite (GridSat) thermal infrared (IR) imagery. On the other hand, the CHIRPS product doesn't merge passive microwave-based precipitation retrievals. It is available at daily temporal (5-daily timescales) resolution that hampers its use in particular highly dynamic precipitation environments [35]. The better performance in the Adige Basin (Italy) and Qinghai-Tibet Plateau might be owing to a dense network of rain gauges applied during its development. The overestimation by ERA-5 is due to the parameterization deficiencies in the controlling procedure of precipitation generation [35], indicating the importance of precipitation frequency corrections supplemented in MSWEP. Generally, JRB receives light precipitation, so retrieving precipitation occurrence at a finer scale is indispensable in capturing light precipitation events. Besides, it is worth noting that because coarser spatial resolution (0.25\({}^{\circ}\)) with considerable model's erroneous characteristic of ERA-5 tends to smoothen precipitation, it underscores considerable overestimation in this region. The findings align with the results of [PERSON] [75], who found that ERA-5 had an overestimation with RMSE values ranging from 0 to 4.5 mm/day across China. The underestimation of the low precipitation intensities and overestimation of higher precipitation intensities indicate that more attention is required when reanalyzing precipitation products in particular complex terrain (Figure 7a-d). These results have also been reported by [PERSON] et al. [74] on the Chinese Mainland. Nonetheless, MSWEP has high spatial (0.1\({}^{\circ}\)) temporal (3 hourly) resolutions, making it more apt to detect even precipitation events at the smaller spatial extent, which are common in this region. Approximately 66% of total precipitation events occur as low-intensity events (<1 mm/day) in this region [25]. The high-frequency sensor-based satellite products can observe low-intensity precipitation events [35; 76].