## 3 Results  
### Evaluation at Annual and Monthly Scales  
The annual average precipitation (835.83 mm/year) in JRB is computed from all available gauging stations from 1981 to 2009. The annual average precipitation, on the other hand, varied greatly between the observed and selected GPPs (Figure S1). The selected GPPs over/underestimated annual precipitation amounts. Four GPPs (MSWEP, APHRO, PGMFD, and CHIRPS) exhibited underestimation (\(-2.61\%\), \(-5.43\%\), \(-5.75\%\), and \(-10.56\%\), respectively), whereas ERA-5 showed significant overestimation (9.23%). Figure 2. depicts the horizontal spatial distribution of mean annual precipitation from gauges and GPPs over JRB. The spatial distribution of gauges showed that the higher annual average amount of precipitation was generally found in the western (from Northwest to southwest, mostly between II and IV) parts of the JRB. In contrast, lower precipitation was distributed in the eastern region (from northeast to southeast, particularly I and III SBs, while SB V has moderate precipitation) with west to east inclination (Figure 2a). Overall, all GPPs were somehow able to capture such precipitation patterns, especially the MSWEP, which offers a better spatial resolution. Likewise, the pattern of gauges, MSWEP, captured an almost concrete pattern that indicated the lower annual precipitation over the eastern part of the basin (Figure 2b). Although ERA-5, APHRO, PGMFD, and CHIRPS exhibited a higher annual distribution in the western regions. Still, these datasets were less accurate in showing the spatial pattern concerning gauge precipitation in JRB. Mainly ERA-5 is represented by the significantly higher amount of annual precipitation compared to the rest of the GPPs (Figure 2c-f). Table 3 represents the metrics list such as CC, RMSE, Bias, and rBIAS (%) of monthly precipitation estimation, which exhibits that the MSWEP and ERA-5 have better performance with higher CC (0.92 and 0.91) and lower RMSE (23.26, 25.48 mm/month), Bias (\(-0.30\), 0.84 mm/month) and rBias (\(-10.48\%\), \(28.06\%\)) compared to other GPPs. In particular, the CHIRPS dataset has the highest value of RMSE, followed by the PGMFD and APHRO (35.09, 32.81, and 28.25 mm/month, respectively). Among all GPPs, the MSWEP, ERA-5, APHRO, and PGMFD had relatively fewer errors in the 12 months. In comparison, the CHIRPS dataset had higher values of \(-1.10\) Bias mm/month. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Indices** & **APHRO** & **CHIRPS** & **ERA-5** & **MSWEP** & **PGMFD** \\ \hline CC & 0.90 & 0.74 & 0.91 & 0.92 & 0.78 \\ RMSE (mm/day) & 28.25 & 35.09 & 25.48 & 23.26 & 32.81 \\ BIAS (mm/day) & \(-0.52\) & \(-1.10\) & 0.84 & \(-0.30\) & \(-0.77\) \\ rBIAS (\%) & \(-18.57\) & \(-27.50\) & 28.06 & \(-10.48\) & \(-20.74\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The statistical metrics for monthly performance evaluation of GPPs in contrast to in-situ gauges data during the entire study period 1981–2009. Figure 2a depicts the monthly comparison of temporal precipitation trends estimated by the GPPs against reference data from January 1981 to December 2009. According to Figure 2a. all GPPs generally have similar trends and are capable of seizing the temporal variability of gauge-based precipitation across the basin. The higher peaks of precipitation in March and July confirm the influence of westerlies and summer monsoons in the region. The slight overestimations were explicitly observed in December with the value of 4.21%, 5.46%, 2.70%, and 1.66% by MSWEP, APHRO CHIRPS, and PGMFD, respectively, and remain underestimated for the rest of the months. Furthermore, in the case of ERA-5 temporal precipitation trends were overestimated throughout all the months. Overall, the GPPs have good agreement in monthly precipitation assessments with the value of CC > 0.7 and lower rBias, except CHIRPs, which showed larger biases and lower correlation.