## 5 Results
### Evaluation Using Monthly Observations from 2000 to 2020  
Daily target and reference precipitation values were employed in the present section to generate monthly accumulated and average precipitations. To this end, monthly accumulated and average precipitations were calculated for each synoptic station and the corresponding GPPs for each month between 2000 and 2020, resulting in over 250 mutual  
Figure 9: Evaluation of six GPPs in the yearly time scale using (**a**) CC, (**b**) RMSE, (**c**) MBE, and (**d**) CSI based on daily observations between 2000 and 2020.  
observations at the station level. Later, these values were incorporated to compute three continuous metrics of CC, RMSE, and MBE at the station level. Their average and SD values were used to examine the capability of the six GPPs in capturing monthly accumulated and average precipitations throughout Iran. Tables 3 and 4 summarize the computed continuous metrics for the GPPs. It is evident that the CC values of all GPPs had improved as the monthly accumulated and average data were used. In this regard, the most remarkable improvement was observed for TMPA, in which its CC values increased from 0.330 (daily observations) to 0.794 (monthly accumulated values). Furthermore, in terms of the CC value when using monthly accumulated precipitations, IMERG obtained the highest value of 0.857 \(\pm\) 0.094, and ERA5 was the second most accurate GPP. The low SD of the CC value for the IMERG suggested that the high CC value was recorded for most of the stations. On the other hand, GSMaP and PERSIANN with 0.767 \(\pm\) 0.137 and 0.752 \(\pm\) 0.122 were two GPPs with the weakest performances. The achieved MBE values suggested that the underestimation and overestimation behavior of GPPs remained similar to daily observations; however, their magnitudes have notably increased. The MBE value of IMERG for monthly accumulated data exceeded 30 mm/month on average, with a relatively high SD value, indicating much higher recorded overestimations. Likewise, TMPA had an MBE value of \(-\)17.65 mm/month with a greater SD value, identifying this GPP as the second-worst GPP in terms of precipitation biases. Moderate overestimations was observed for ERA5, and CHIRPS, GSMaP, and PERSIANN had more satisfactory performances. Based on the RMSE values, the GPPs performance can be ranked as GSMaP, ERA5, CHIRPS, PERSIANN, and IMERG, with the highest and lowest RMSE values of 22.73 mm/month and 50.89 mm/month. Furthermore, the findings revealed that when using monthly average precipitations, IMERG and ERA5 are the two highest GPPs, while this does not hold for the other two metrics. In fact, PERSIANN and GSMaP were recognized as the best-performing GPPs in terms of MBE and RMSE, respectively. yearly precipitations to assess the performance of the GPPs. The obtained average and SD values of CC, RMSE, and MBE based on yearly observations at the station level are provided in Tables 5 and 6. The findings suggest that the computed CC values increased for all GPPs in comparison to using daily observations, except for GSMaP. Based on yearly observations, IMERG and GSMaP were identified as the most and least accurate GPPs, and the second to fifth ranks were for ERA5, TMPA, CHIRPS, and PERSIANN, respectively. However, when considering MBE, PERSIANN and GSMaP had the two lowest average MBE magnitudes of 34.79 mm/year (overestimation) and 44.23 mm/year (underestimation), with the lowest MBE SD for GSMaP, proving its more stable characteristics throughout the country. On the other hand, TMPA and IMERG underestimated and overestimated the yearly precipitation values by about 200 mm/year and 360 mm/year, respectively. In terms of the RMSE, the lowest value was acquired by CHIRPS and was very closely followed by GSMaP; however, considering their RMSE SD values, GSMaP had the capability of providing more consistent precipitation estimates across the country.