## 4 Discussion  
The application of gridded precipitation products is common in recent hydrological and atmospheric studies [42; 43]. However, the spatiotemporal accuracy of precipitation products plays a significant role in obtaining reliable results. Recently, various studies reported considerable differences in the accuracy of precipitation estimation at the spatial and temporal scales [16; 18; 44]. In the current study, four gridded global precipitation products were evaluated in Pakistan to find the spatial and temporal agreement with ground observations using four statistical matrices, including CC, RMSE, KGE, and PBias, during 2003-2020. The temporal scale assessment revealed the good agreement of the GPPs with the ground observations at a monthly scale rather than a seasonal scale, which is confirmed by a previous study in the northern region of Pakistan [6]. [PERSON] et al. [45] also reported the better agreement of IMRG-V06 and other selected precipitation products at the monthly scale with reference data. This shows that all the datasets are more skilled at estimating precipitation at a high temporal resolution than at a seasonal or annual scale. The study by [PERSON] et al. [46] also exhibited the significant variation of global datasets for seasonal precipitation estimation over arid Central Asia; however, they found better agreement between ground observations and GPPs at a seasonal scale than a monthly scale, which contradicts the current findings. The post-monsoon season (October-November) is also considered the dry season due to the least rainfall yielded across the country [47]. During this season, the datasets exhibit poor performance compared to the other seasons, with higher PBias and negative KGE values. The overestimates of precipitation by ERA-5, IMRG7, and MSWEP2.8 during this season are presumed to be limitations of satellite retrieval algorithms and seasonal biases in the model in estimating daily and light precipitation. The uncertainty of the products in detecting light and moderate precipitation events was also reported by [PERSON] et al. [45]. However, [PERSON] et al. [18] also noticed the overestimation of ERA-5 in this season, but the performance of the dataset was relatively better than in the monsoon season. The finding of [PERSON] et al., [48] also contradicted with the current finding, where MSWEP shows better performance in the winter, pre-monsoon, and post-monsoon seasons than in the monsoon season. According to [PERSON] [6], the IMRG has better agreement with the reference data in the post-monsoon season than in the monsoon season. Furthermore, the performance of the GPPs was relatively better in the winter and post-monsoon seasons than in the monsoon, which depicts the products having good precipitation estimation skills in normal rainfall conditions. During monsoon season, heavy rainfall occurs, with extreme events in the country, which may influence the model's accuracy as the datasets' results overestimate the precipitation. Several studies reported significant overestimations of the GPPs during the monsoon season and less deviation from the RG data during the winter season in Pakistan [17; 18]. Additionally, all the precipitation products show the tendency to overestimate, with different levels of deviation from the ground observations, except CRU TS, which underestimated during the pre-monsoon and post-monsoon seasons. The spatial performance evaluation revealed that none of the GPPs had similar and smooth performance over the country. This may be due to the significant diversity in topographic conditions and climatic variations across the country. Such variations also have a close association with the precipitation pattern in this region [49]. A significant number of studies already reported the influence of complex topography and diverse climatic conditions on the dataset's accurate precipitation measurements [4; 8; 18; 33; 37; 39]. Each dataset performedwell in the southern region, including Punjab, Sindh, and Balochistan provinces.