## 4 Discussion
These findings support the current study and provide evidence of the influence of topography and climatic conditions on GPPs' reliability. Overall, IMERG7 shows superior performance at both spatiotemporal scales. MSWEP 2.8 also shows a nearly similar performance to IMSWEG 7, whereas ERA-5 demonstrates considerably poorer performance in GB and KPK provinces. CRU TS shows overall moderate and comparatively weak agreement with the ground observations in Punjab, Sindh, and Balochistan provinces. ERA-5 is a reanalysis model-based precipitation product that considers various land surface parameters for precipitation simulation. In the high-elevation regions or arid regions, the model struggles to assess the soil moisture, soil temperature,and snow cover [46], which may lead to precipitation overestimates. Several studies confirmed that ERA-5 overestimates across different regions of the world, including over the Himalayas [55], the relatively wetter and complex terrain region of Turkey [56], the Assimiboine River Basin [57], arid Central Aisa and its subregions [46], and in the mainland of China [10]. In contrast, the better performance of ERA5 was reported in various studies over Pakistan [18; 58]. The resolution of ERA5 is 0.25\({}^{\circ}\) and it is based on a global atmospheric model, which can provide more accurate general atmospheric patterns and trends, while ERA-5 (ERA5-Land) is a recently upgraded version of ERA5 with a finer resolution (0.1\({}^{\circ}\) degrees) that focuses on land surface processes [35]. The high resolution may increase the noise in precipitation estimation, especially when the model is not well tuned to local microclimates or topographic variation. The outperformance of IMERG7 is mainly associated with the integration of multiple satellite data and the continuous calibration of estimates, along with regular improvement in the algorithm. The significant association of IMERG7 with ground observations has been reported in several studies [6; 59; 56]. MSWEP2.8 merges multiple satellite, reanalysis, and ground observations data, along with sophisticated bias correction techniques, which enhances the accuracy of its precipitation estimates over other global products. [PERSON] et al. [8] assessed the performance of MSWEP against other global products in the highlands of the Indo-Pak region and their findings align with the current study. Overall, CRU TS showed a moderate association with the reference data, but this performance was not consistent and uniform along the elevation gradients in this study so, it is difficult to interpret the influence of elevation and climatic conditions on the dataset. CRU TS is a ground station rain data-interpolated product; its performance relies on the density and uniform distribution of ground stations [5; 11]. Thus, it can be presumed that the moderate performance of CRU TS over Pakistan is due to the limited number of rain gauges and their non-uniform distribution across the country. Additionally, for this study, we made significant changes to its original resolution (0.5\({}^{\circ}\)) to very high resolution (0.1\({}^{\circ}\)) to match the grid size with other selected GPPs. Such modifications most possibly influence the precipitation estimation potential of the dataset. Moreover, besides GPPs' capabilities to capture accurate precipitation, the influence of the resampling technique and the quality and density of the ground observations cannot be ignored in validation studies.