## 6 Discussion
### Discussing and Comparing the Results  
This paper evaluated six GPPs using in situ observations of synoptic stations from 2000 to 2020 throughout Iran. Both continuous and categorical metrics were considered for the validation process to benefit researchers in different disciplines. The findings, in general, suggested a discrepant performance of all GPPs, which was anticipated since each GPP was generated using multiple sources, different approaches and algorithm principles for blending the sources, and different bias adjustment procedures. In total, the findings pointed out that TMPA and GSMaP underestimated the precipitation values, while other GPPs, i.e., PERSIANN, CHIRPS, ERA5, and IMERG, overestimated precipitation values with spatial discrepancies across Iran (see Tables 2-6 and Figure 5). These were in agreement with previous investigations globally and regionally [99; 100; 101; 51; 102; 103; 104]. Considering daily data that contained over 1.7 million observations, GSMaP was identified as the first accurate precipitation product in Iran over the last two decades, which was similar to findings by [PERSON] et al. [35] in China. In particular, GSMaP obtained the lowest RMSE and MAE values with the highest CSI value, supporting the superior performance of this GPP over others compared in this study. This may be traced back to the utility of both infrared and microwave data sources, as well as daily bias adjustment to generate GSMaP GPP [15; 19]. ERA5 was identified as the second most accurate GPP, suggesting the acceptable performance of this reanalysis precipitation product throughout Iran. Although both IMERG and GSMaP are emerging GPPs from the GPM mission, the daily observations indicated a higher overestimation and biases of IMERG. It could probably be associated with the monthly bias adjustment of IMERG, while the GSMaP uses daily adjustments, and this could be a prospect to improve IMERG performance. The daily observations at the station level also supported the findings when using the whole time-series observations. Furthermore, the station-level analysis revealed that overestimation/underestimation with larger magnitudes occurred in regions with higher precipitation rates. Therefore, such knowledge could be effectively incorporated when working on GPPs' enhancement. In other words, the precipitation rate (i.e., yearly) could be considered as an input factor, along with other relevant factors, to apply recalibrations and improvements to GPPs using statistical and machine learning algorithms [105; 106]. The investigations on the monthly time scale using daily observations revealed the better performances of all GPPs in dry months based on lower RMSE and SD values, which can also be related to lower precipitation rates in these times. However, this did not hold for precipitation occurrence detectability since all GPPs had their weakest performances in dry months based on categorical metrics. This suggests that future studies could be shaped on improving the precipitation intensity and occurrence enhancement of GPPs in wet and dry months, respectively. Categorical metrics in all months suggested the premiere performance of GSMaP, while the continuous metrics indicated the better performance of ERA5 in some months. The yearly time scale comparisons (Section 5.1.3) illustrated that, in general, the quality of GPPs remained the same over the past two decades in Iran. In particular, the consistent and stable CSI values indicated no improvement in precipitation occurrence detectability for all GPPs, which was in contrast with another study conducted in China [35]. The discrepancies may lie in the geographical conditions of the study areas, the implemented frameworks, and the amount of international collaboration of countries in sharing the in situ data for global datasets that are employed for bias adjustments.