## 7 Conclusions  
In this paper, six well-known Gridded Precipitation Products (GPPs) were evaluated using two decades of in situ observations (over 1.7 million) throughout Iran. The initial examinations revealed that over 81% of synoptic stations were independent of those incorporated for the calibration or bias correction of GPPs. The evaluation results based on daily observations suggested the better overall performance of GSMaP in the last two decades. Considering the whole time series, TMPA and GSMaP had underestimation, while the other GPPs overestimated the precipitation values. However, the further spatial analysis at the station level revealed that all GPPs, except TMPA, had diverse error characteristics throughout Iran. In other words, the GPPs include both underestimations and overestimations in different regions, with the dominance of one. It was also observed that for all GPPs, the overestimation and underestimation magnitudes were linked to the precipitation rates with the same behavior. Furthermore, the daily observations at the monthly time scale illustrated that all GPPs obtained better quantitative values in dry months based on continuous metrics, while the categorical metrics suggested the reverse. The yearly comparisons demonstrated that the GPPs' capability, in general, remained the same over the past two decades, except for the MBE of GSMaP, which presented a significant improvement. The monthly and yearly accumulated precipitation values from the GPPs represented large errors, i.e., RMSE and MBE, indicating that the utility of these values would introduce high uncertainties to any models or results. Therefore, the monthly or yearly average values could better suit similar applications, or the original monthly/yearly precipitation products could provide more accurate results. Additionally, the yearly precipitation values illustrated the superior performance of CHIRPS, and GSMaP was ranked second in this regard. Although, on the whole, this paper identified GSMaP as the best-performing GPP in Iran, other factors (e.g., temporal resolution and spatial resolution) that were discussed should also be taken into account for GPP preferences. Moreover, the general implication of this paper was that even though different GPPs have emerged, they do not include highly accurate precipitation estimates, and more future works are required to improve their performances or merge multiple products to obtain new precipitation products with better accuracy.  
**Author Contributions:** Conceptualization, A.G., A.M. and S.J.; methodology, A.G.; software, A.G.; validation, A.G.; formal analysis, A.G.; investigation, A.G.; resources, A.G.; data curation, A.G.; writing--original draft preparation, A.G.; writing--review and editing, A.G., A.M., S.J. and Z.D.; visualization, A.G.; supervision, A.M. and S.J.; project administration, A.G. and S.J.; funding acquisition, S.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The Gridded Precipitation Products (GEE) supporting the reported results of this paper are publicly available from the Google Earth Engine (GEE) data catalog. In situ precipitation observations were obtained from the Islamic Republic of Iran Meteorological Organization (IRIMO).  
**Acknowledgments:** The authors would like to sincerely appreciate the support of the Islamic Republic of Iran Meteorological Organization (IRIMO) for kindly providing the in situ precipitation values from the synoptic stations throughout Iran. During this study, [PERSON] was funded by the Erasmus + ICM programme for a 5-month stay at Lund University, Sweden.  
**Conflicts of Interest:** The authors declare no conflict of interest.