## 4 Discussion
The MSWEP is integrated the gauge, satellite, and reanalysis along with precipitation estimations contingent on IR data from the GridSat B1 archive to improve precipitation estimations, particularly in the convection-influenced areas, by employing a parsimonious CDF-matching method. Such a product can estimate the light precipitation more precisely than other GPPs because of the increased sensitivity of the cloud-top IR from the GridSat and high-frequency channels [35]. The existing literature review suggests that the performance evaluation of newly launched products (MSWEP) has been investigated worldwide. Where it exhibited moderate agreement with reference (ground-based) data in Qinghai-Tibet Plateau [69]; however, a better correlation was observed in the highlands of JRB (in this study), Australia, and Africa [11], and southeastern south America [72]. This is due to the fact that MSWEP is a global precipitation dataset intended primarily for hydrological modeling [35]. The data is designed to optimally combine the best quality rainfall data sources. In the first step, the long-term mean of MSWEP is derived from the Climate Hazards Group Precipitation Climatology (CHPclim) dataset, which was just recently released. CHPclim is a worldwide precipitation product using gauge observations and satellite data with a resolution of 0.05\({}^{\circ}\). In the second step, orographic impacts are removed using data from various sources, such as the 13,762 catchment stations located worldwide. Seven datasets determine the temporal variation of MSWEP: two gauge-based observations (CPC Unified and GPCC), three satellite-based products (CMORPH, GSMaP-MVK, and TMPA 3B42 RT), and two atmospheric model reanalyzes (ECMWF Interim Reanalysis and the Japanese 55-year Reanalysis). Up to the present, estimated precipitation estimates from MSWEP exhibited a good correlation with reference (ground-based) data in our study area. Conversely, the APHRO underestimated precipitation magnitudes because it uses the non-representative low-altitude conventional observatories with lower (0.25\({}^{\circ}\)) resolution to reproduce the spatial distribution of high-altitude precipitation [26]; another possible reason might be wind-induced precipitation under-catch that have not been adjusted in APHRO product. Similar results are presented by [PERSON] et al. [26] over UIB suggested that precipitation estimated from APHRO shows substantial underestimations and is not suitable to use directly in hydro-meteorological studies without correction, despite the fact that their study area is larger than the current study and this area was excluded. However, this product is derived from reference gauges; thus, it underscores a higher correlation. The MSWEP displayed better agreement with daily gauge precipitation data in the JRB, which might be owing to the integration of four categories of product and high spatial and temporal (3 hourly) resolutions. Another possible reason for ERA-5 overestimation in this region might be the somewhat contribution of wind-imputed errors and increasing concentration of aerial aerosols [25; 77]. The aerosol concentration in the environment has been increasing in Asia, which can interrupt the precipitation and thus can decrease its magnitude [78; 79]. Previous studies also pointed out that high aerosol concentration can influence the cloud drop size, which is ultimately a cause of overestimation by satellite sensors [25; 80]. To improve the precipitation retrieval algorithm of the ERA-5 product, the algorithm developers should also consider the influence of aerial aerosols. Based on our results, the bias correction of GPPs' daily precipitation estimates with referenced data is recommended before it can be enforced in direct applications, particularly in the glaciated region.