## 4 Discussion
In contrast, moderate and worse performances were observed in the KPK and GB provinces, respectively. The GB region is characterized by glacier-rich high mountains, complex topography, and harsh climatic conditions area [50], which may induce orographic effects and impact satellite algorithms, as well as conditions critical for accurate modeling of precipitation estimates [6; 8; 30]. Furthermore, the limited number of RGs and the quality of their precipitation records also cannot be ignored during the evaluation of the products, because extreme weather conditions and wind-induced errors make a significant contribution to misreading the total rainfall at high-elevation stations [51; 52]. Thus, the installation of more observation stations and their proper validation is needed to reduce uncertainties. Furthermore, ERA-5 and IMERG7 show a tendency to overestimate across all the provinces, while MSWEP2.8 underestimates precipitation in KPK, Punjab, and Sindh provinces, and CRU TS exhibits the same tendency in Punjab, Sindh, and Balochistan provinces. To further explore the main causes of the spatial variation in the products' skills, the study area was divided into four main regions based on elevation. Significant variation was observed for all the products' performance between region I and the rest of the regions, where they exhibited weak correlation, considerable overestimations, and negative (for CRU TS, ERA-5, and MSWEP2.8) and very low (for IMERG7) KGE values. Except for CRU-TS, the rest of the products improved their performance from higher elevations to low-elevation plain areas. The topographic and climatic characteristics of region I are extraordinarily different from the rest of the regions due to the high elevation (3000-8000 m), with rough topography, extreme cold winters, snow, and glacier cover. Such conditions across various regions of the world challenge the performance of precipitation products and are reported in several studies [32; 33; 53; 54]. [PERSON] et al. [32] compared the performance of GPPs in the evaluation-based zones in Pakistan, including the glacier region, western mountains, and plain areas. They revealed that in the northern high-elevation region, all the selected GPPs expressed significant deviation from the RG data, which may be due to the low surface temperature and radiation absorption over snow and ice cover, resulting in the misidentification of rainy clouds and wrong precipitation estimations. Despite the same elevation of 500-3000 m in the sub-northern mountain area (region II) and the western high-elevation area (region III), ERA-5, IMERG7, and MSWEP 2.8 show relatively less RMSE and higher KGE in region III, depicting the influence of climatic factors on accurate precipitation estimations. Region II falls under the humid climatic region of Pakistan, where it receives 900-1485 mm of precipitation annually; in contrast, the annual rainfall in region III is 100-400 mm [47]. Heavy rainfall events in monsoon season and snowfall in winter season in region II may be influencing the prediction ability of the products and be the possible reason for their performance variation between both regions. The same behavior is observed for other precipitation products in the humid region of Pakistan, mainly during the monsoon season, and high precipitation intensities and magnitudes are also reported by [PERSON] et al. [17]. The correlation between the GPPs and the reference data was nearly the same in the western mountains and plain area, which show the products have good skills in capturing the precipitation trends at both elevations. However, based on the RMSE and KGE values, all the datasets show better performance at an elevation range of 0-500 m. [PERSON] et al. [53] evaluated the performance of MSWEP2.8, IMERG, ERA5, and other GPPs at various topographic gradients in Colombia and found better agreement between GPPs and ground observations at 0-1000 m elevation on a monthly scale.