## 5 Discussion  
The application of remotely sensed precipitation data is important in assimilation, estimation and forecast and for improving model simulation skill to better understand land-atmosphere interactions in regions where in-situ data is sparse. Comparisons of GPPs with available precipitation records are crucial to determine their strengths and limitations in such regions. The current study used in-situ records to validate GPPs across a range of climate, land cover types and terrain in Pakistan. The in-situ records may also be subject to physical and non-physical differences and efforts were made to reduce their effects. The precipitation patterns for both westerlies and monsoonal precipitation were consistent among all GPPs and agreed with previous results [14; 36; 37]. The seasonal scale magnitudes of precipitation reported by the GPPs were mostly larger than in-situ in the northern region, while they performed relatively well in the other regions. PERSIANN-CDR consistently overestimated in-situ precipitation, while TMPA, CHIRPS and MSWEP performed adequately. These findings concurred with those of [35; 47; 54; 58], with a possible reason being that infrared based retrievals have limitations in estimating precipitation over complex terrains [35; 59; 60]. The daily scale performance suggests that GPPs were consistent for daily scale thresholds, with PERSIANN-CDR and MSWEP overestimating in some regions. Daily scale performance is improved for precipitation events of \(\geq\)10 mm/day in all regions, which concurred with the results of [47; 61]. At monthly time scale, GPPs usually overestimated precipitation events, with lower R values, high RMSD values and differences in mean for different regions. At southern parts of the region relatively good performance was observed in terms of the linear regression with in-situ and improved descriptive statistics. The differences in performance on monthly scale could possibly be due to the complex terrain induced deviations, precipitation magnitude, in-situ stations uncertainties and their low density, which did not accurately represent the precipitation pattern in such regions [18; 62]. GPCC precipitation products may also be subjected to uncertainties, which are derived from in-situ stations, thus climatologies developed from limited number of in-situ stations in such regions might not be accurate estimation of the regional precipitation behaviors [53; 63]. Calibration and validations of GPPs with GPCC products in such data scarce region might be subjected to noise and deviations. Infrared and microwave based algorithms also have limitations due to terrain and wet and dry regional climates [64; 65; 66; 67]. At monthly time scale, the performance of GPPs is relatively improved from daily time scale, however the deviations in error metrics were also obvious and consistent at regions with more precipitation. At interannual time scale, the results indicated that the temporal agreement of GPPs varied at both regional and seasonal scale. In the northern regions, the performance of GPPs was unsatisfactory both in terms of temporal agreements and RMSD values, with similar findings also reported by [28; 53]. In the southwestern parts of the country, the overall temporal agreement of the GPPs agreed with that of in-situ. The statistical metrics had consistent values in terms of interannual variations and positive linear relationship with in-situ during the high precipitation seasons. The differences in precipitation magnitudes were higher in regions and during seasons with higher precipitation amounts. The RMSD values of stations located in the northern parts of the study area were usually higher than those stations located in central and southern regions [34; 35; 36; 66]. TMPA produced the highest R values and lowest RMSD values, followed by MSWEP and CHIRPS. PERSIANN-CDR displayed similar dynamics regarding interannual scale precipitation variation, although the RMSD values during the peak precipitation season were much higher than for the other GPPs in all regions [47; 53].