## 5 Results
### Evaluation Using Daily Observations from 2000 to 2020
#### 5.1.1 Daily Time Scale  
All daily corresponding precipitation values (i.e., nearly 1.7 million observations) were compared to achieve a general overview of the performance of the selected GPPs (see Figure 3). This comparison considers no spatial or temporal characteristics, and includes all precipitation values, and thus, only provides merely a broad depiction of GPPs' performance over the last two decades. Large scatterings indicate relatively imperfect agreements between target and reference precipitations. Figure 3 shows that the precipitation values were mainly low, especially less than 20 mm/day, as anticipated based on the daily precipitation values in Iran. Visually, it can be seen that the TMPA precipitations (Figure 3a) had the least variation (i.e., standard deviation), while the IMERG precipitation values (Figure 3e) had the highest variation. Generally, Figure 3 shows that the target and reference precipitation values were closer to the identical reference line for ERA5 and GSMaP GPPs. In particular, ERA5 (Figure 3f) obtained the highest CC of 0.623, closely followed by GSMaP with a CC of 0.599, and IMERG (CC = 0.483), PERSIANN (CC = 0.356), CHIRPS (CC = 0.353), and TMPA (CC = 0.330) were in the subsequent ranks. Moreover, Figure 3a-d illustrate that TMPA and GSMaP had an underestimation when retrieving the precipitation values. These also are in agreement with the calculated MBE values, which were \(-\)0.574 mm and \(-\)0.132 mm for TMPA and GSMaP. On the other hand, IMERG, ERA5, CHIRPS, and PERSIANN overestimated the precipitation values with the MBE values of 1.001 mm, 0.268 mm, 0.152 mm, and 0.094 mm, respectively. Table 2 summarizes the quantitative values of both continuous and categorical metrics that support the perceptions drawn from Figure 3. The GSMaP and TMPA achieved the first- and second-lowest MAE values of 0.834 mm and 0.913 mm, while other GPPs with overestimation characteristics obtained higher MAE values, with the highest value of 1.791 mm for IMERG. Considering the RMSE metric, the GSMaP obtained the lowest value (3.48 mm), and the second most accurate GPP was ERA5, with an RMSE value of 3.59 mm. IMERG and CHIRPS, on the other hand, were less accurate by acquiring the RMSE values of 6.89 mm and 4.68 mm, respectively. Furthermore, the POD values indicated that ERA5 (POD = 0.867) had the best performance, followed by GSMaP (POD = 0.784), although the FAR metric suggested the exact opposite. In particular, ERA5 and GSMaP obtained FAR values of 0.480 and 0.568, respectively, indicating that GSMaP performed the best among the other GPPs. The obtained POD values illustrated the weakest performance of TMPA and CHIRPS; while considering the FAR metrics, their performances were second to GSMaP and closely similar to ERA5. With respect to CSI, GSMaP was identified as the most accurate GPP in precipitation occurrence identification, with a CSI value of 0.454, followed by ERA5, IMERG, PESIANN, CHIRPS, and TMPA. The findings support that GSMaP outperformed other GPPs, and both TMPA and CHIRPS were identified as the least suitable GPPs in the last two decades. The daily comparisons were also adopted at each synoptic station individually. The boxplots of six metrics, including CC, RMSE, MBE, POD, FAR, and CSI, are presented in Figure 4. Figure 4a shows that GSMaP and ERA5 outperformed the other GPPs by generally obtaining higher CC values at each station, while the TMPA presented the weakest performance. Moreover, Figure 4b clearly illustrates that the IMERG achieved higher RMSE values at each station, supporting the previous finding reported in Table 2. Other GPPs manifested relatively similar performances when considering RMSE at each station, with the best performance for GSMaP, followed by ERA5, TMPA, PERSIANN, and CHIRPS. Likewise, Figure 4c presents MBE, indicating the existence of overestimation and underestimation possibilities in all GPPs, except for TMPA, in which almost all stations had