## 5 Conclusions  
The current study conducted a baseline and timely assessment of the recently launched precipitation product MSWEP (V2.2) in the highland transboundary region of Indo-Pak (JRB) at the common period from 01-January 1981 to 31-December 2009 by comparing with referenced (in-situ gauges) data at spatial (entire basin and SBs) and temporal (annual, monthly, daily, and seasonal) scales. For its systemic performance comparison, four other products, APHRO, ERA-5, CHIRPS, and PGMFD, were also evaluated parallel to MSWEP. The continuous statistical metrics (CC, RMSE, BIAS, and BIAS (%)) and categorical indices (POD, FAR, and CSI) were applied to examine their accuracy and contingency. The key findings of this study are:  
1. All GPPs exhibited spatial variability of entire annual precipitation during the whole period, with high and low amounts of precipitation in northwestern and northeastern parts of JRB, respectively. Similarly, monthly precipitation estimates with temporal variability trend well performed by GPPs, with slight overestimation during winter (specifically in December) and underestimation in the next two seasons. However, ERA-5 displayed a significant overestimation amount of precipitation at all scales. 2. The correlation of all GPPs is better with in situ gauges at daily and seasonal spans, but the CHIRPS showed a smaller (<0.6) correlation, indicating its feebleness. Therefore, more efforts must be employed for its improvement in respective periods. Comparing the current findings with existing findings on different regions of the world, we realized that the performance of GPPs is highly dependent on topographical and atmospheric settings. Since the latest MSWEP product integrates the rain gauge, reanalysis, and satellite dataset to make it most advanced by minimizing these disadvantages thus, its performance was better in all seasons (with slight overestimation in winter). 3. Overall, the GPPs performed well in the monsoon season compared to the winter and pre-monsoon seasons. Better performance on a daily scale is observed in SBs V, IV, the upper part of SB I, and some western portion of SB III, whereas poor performance can be found in the middle and southeast portion of SB III and the lower portion of SB II. 4. The MSWEP exhibited better skills in terms of precipitation detection capability, followed by ERA-5, APHRO, and PGMFD, with approximately 51-72% of success ratio in sensing realistic and unrealistic precipitation events. Conversely, the CHIRPS had a larger FAR than other GPPs. The precipitation detecting ability was best in the monsoon season compared to the next two seasons. 5. All selected GPPs showed a tendency to overestimate light precipitation (0-1 mm/day) and underestimate moderate to heavy precipitation, whereas the ERA-5 tends to underestimate the light but overestimate moderate (1-20 mm/day) and heavy precipitation (>20 mm/day) events. The CHIRPS showed less accuracy in detecting mainly precipitation events, significantly overestimating unrealistic precipitation (0-1 mm/day) events and underestimating moderate to heavy precipitation events. On the other hand, the MSWEP had a better ability to capture light, medium, and high precipitation intensities than all other GPPs. These results suggest that MSWEP has superior overall performance products. Conversely, the CHIRPS divulges poorer performance; thus, it needs more improvement efforts to enhance its ability to detect light precipitation more accurately. Based on the aforementioned results, we found that the performance of the newly launched precipitation dataset (MSWEP) was better than all other products. The results of this study provide a better understanding of the performance of global GPPs at a local scale that can assist hydro-meteorological applications in such glaciated types of regions.