## 5 Results
### Evaluation Using Daily Observations from 2000 to 2020
#### 5.1.1 Daily Time Scale
In contrast, GSMaP provided more precise precipitation  
Figure 4: Boxplots of three continuous metrics, including (**a**) Correlation Coefficient, (**b**) Root Mean Square Error, and (**c**) Mean Bias Error, and three categorical metrics, including (**d**) Probability of Detection, (**e**) False Alarm Ratio, and (**f**) Critical Success Index at the daily time scale between 2000 and 2020.  
estimates since 194 stations (79%) had RMSE values of less than 4 mm. The performance of GSMaP was followed by those of ERA5, TMPA, PERSIANN, and CHIRPS. Further investigations revealed that stations with higher RMSE values were mainly located in regions with higher precipitation rates. Figure 5 (right column) shows the spatial variability of the average biases (MBE) at each station for the GPPs. On the whole, all GPPs presented diverse performances and included stations with overestimation and underestimation, except for TMPA and IMERG, with almost dominant behaviors in underestimating and overestimating precipitation values, respectively. Underestimation at almost all stations was obtained for TMPA, with dominant MBE values in the range of \(-1\) to 0, indicating low biases throughout the country. Conversely, IMERG exhibited a relatively much higher MBE and included 241 (98%) stations with overestimation, of which the dominant number of stations were fallen in MBE ranges of 0-1 mm (50%) and 1-2 mm (46%). Based on Figure 5 (right column), CHIRPS, PERSIANN, and ERA5 presented relatively similar patterns, with, respectively, 167 (69%), 181 (74%), and 188 (77%) stations with overestimation and the remnant underestimated precipitation values. For GSMaP, most of the stations were assigned to MBE ranges of -1 mm and 1 mm, of which 158 (65%) and 77 (31%) stations had underestimation and overestimation, resulting in the best performance among other GPPs in total. Figure 5: Obtained CC (**left** column), RMSE (**middle** column), and MBE (**right** column) values for the GPPs at the station level using daily observations from 2000 to 2020. Complementary to the spatial evaluation of the selected GPPs using continuous metrics, Figure 6 presents the obtained CSI values at the station level based on two decades of daily observations. At a glance, Figure 6 shows that for all GPPs, the weakest performances occurred in the central parts of the country. Furthermore, the findings indicate the superior performance of GSMaP in comparison to other GPPs. In particular, over 34% and 61% of stations fell in the CSI ranges of [0.2-0.4) and (0.4-0.6], respectively, for GSMaP, which proved its potential capability to capture the precipitation occurrence. Meanwhile, ERA5 was identified as the second most accurate GPP, with approximately 50% and 47% of stations in the CSI ranges of [0.2-0.4) and (0.4-0.6], respectively. For IMERG, as the third most accurate GPP in terms of precipitation occurrence detection, the dominant proportion (86%) of stations achieved CSI values in the range of [0.2-0.4), and a minority of stations had upper and lower CSI values. For the other three GPPs, 77% (TMPA), 83% (CHIRPS), and 85% (PERSIANN) of stations acquired CSI values between 0.2 and 0.4, and almost all of the other stations obtained lower CSI values. Based on the results, these GPPs were ordered as PERSIANN, CHIRPS, and TMPA, considering the accurateness in the precipitation occurrence detection at the station level.