## 5 Results
### Evaluation Using Daily Observations from 2000 to 2020
#### 5.1.1 Daily Time Scale
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Precipitation Product** & **CC** & \begin{tabular}{c} **RMSE** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **MBE** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **MAE** \\ **(mm)** \\ \end{tabular} & **POD** & **FAR** & **CSI** \\ \hline TMPA & 0.330 & 4.10 & \(-\)0.574 & 0.913 & 0.368 & 0.564 & 0.249 \\ CHIRPS & 0.353 & 4.68 & 0.152 & 1.220 & 0.369 & 0.564 & 0.250 \\ PERSIANN & 0.356 & 4.25 & 0.094 & 1.262 & 0.742 & 0.668 & 0.297 \\ GSMaP & 0.599 & 3.48 & \(-\)0.132 & 0.834 & 0.784 & 0.480 & 0.454 \\ IMERG & 0.483 & 6.89 & 1.001 & 1.791 & 0.693 & 0.621 & 0.324 \\ ERA5 & 0.623 & 3.59 & 0.268 & 1.027 & 0.867 & 0.568 & 0.405 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The continuous and categorical metrics calculated from all corresponding precipitation observations to provide a general overview of GPPs performance in Iran over the last two decades. Figure 3: Scatterplots of daily reference precipitations and target precipitations of **(a)**TMPA, **(b)** CHIRPS, **(c)** PERSIANN, **(d)** GSMaP, **(e)** IMERG, and **(f)** ERA5 for the study period of 2000 and 2020.  
underestimations. Meanwhile, more considerable variation and magnitudes in MBE at all stations are evident for IMERG, which agrees with the obtained results when considering all corresponding precipitation values (MBE = 1.01 mm). Figure 4d shows that PERSIANN, GSMaP, IMERG, and ERA5 had relatively similar behaviors when considering POD values at the station level with the superiority of ERA5 and GSMaP, respectively, while TMPA and CHIRPS had poor performances. On the other hand, the FAR boxplot (Figure 4e) shows rather close performance patterns for all GPPs, with the best and worst performances for GSMaP and PERSIANN. Finally, the CSI metric demonstrated that the GSMaP, followed by ERA5, IMERG, and PERSIANN, had the best performances, respectively, while the two other GPPs had similar weak performances (see Figure 4f). In addition to presenting the statistical metrics at the station level through boxplots, they are also illustrated based on geographical coordinates. This analysis enables a more profound investigation of the spatial variability of GPPs performance in Iran. Figure 5 shows the calculated continuous metrics, presenting the precipitation intensity accuracies at each station. It is worth noting that the country's second-level basins boundaries (in black line) are also overlaid to benefit the individuals working on smaller-scale studies (i.e., one or more basins). In general, all GPPs had the best performances in the western part of the country; however, GSMaP, ERA5, and IMERG presented considerably more applicable performances throughout Iran. In terms of CC (Figure 5--left column), for TMPA, the best performances were observed for 81 (33%) stations in CC ranges of 0.4-0.6. However, the remnant (67%), mostly located in the northern parts of the country with higher precipitation rates and central regions, were in lower CC value ranges. Relatively similar patterns were also observed for CHIRPS and PERSIANN with slightly better performances, in which 38% and 40% of stations were assigned in the same CC range, and very few stations obtained higher CC values. However, three other GPPs, i.e., GSMaP, ERA5, and IMERG, manifested considerably better performances by including many stations with higher CC values. Particularly, ERA5, with 127 (52%) stations in the CC range of 0.6-0.8, followed by GSMaP and IMERG, with 106 (43%) and 55 (22%) stations in the same range, provided more accurate precipitation. Likewise, Figure 5 (middle column) illustrates the spatial pattern of the obtained RMSE values at each station for the GPPs. Considering the higher values of RMSE as an adverse indicator of GPPs performance, IMERG included 161 (65%) stations with RMSE values over 4 mm.