## 4 Results
### Evaluation of Temporal and Spatial Dynamics
Overall, TRMM product indicated better quantitative performance and showed reasonable consistency against reference precipitation over the study region. Figure 4: Statistical indicators for the assessment of **(A)** (GB) and **(B)** (SB) precipitation products against reference data (Annual timescale). The GB and SB precipitation products were further evaluated during winter monsoon season. All of the GB products underestimated the winter precipitation with the highest and lowest accuracy being observed in CRU and CPC product with the estimated mean error of \(-25.08\) (\(-20.49\%\)) and \(37.99\) mm (\(-31.04\%\)), respectively. In contrast, the SB products overestimated the precipitation magnitude except CHIRPS product, which indicates the underestimation of precipitation amount over the study region. The highest and lowest accuracy was observed in TRMM and GSMap products with the estimated mean error of \(24.06\) (\(19.39\%\)) and \(80.66\) mm (\(65.03\%\)) respectively. The rest of the precipitation products also showed better quantitative agreement with a higher range of deviation from the mean observed precipitation. Overall, the performance of CRU and TRMM were consistent and show relatively less error during the winter monsoon season. Furthermore, the evaluation of precipitation products during the summer monsoon season indicated the similar pattern of statistical metrics as assessed during annual time scale. The summer monsoon precipitation dominated the regional water balance and plays a significant role in annual precipitation variability. All of the GB and SB products underestimated the observed precipitation. The highest and lowest accuracy in GB products was observed in GPCC and CPC with the estimated mean error of \(-50.03\) (\(-16.17\%\)) and \(-114.50\) (\(-36.48\%\)), respectively. Moreover, the TRMM and GSMap showed the highest and lowest accuracy as compared with in-situ with the estimated mean error of \(-15.18\) (\(-4.48\%\)) and \(-136.06\) mm (\(-40.16\%\)), respectively. The products underestimation was more  
Figure 5: Statistical indicators for the assessment of (**A**) (GB) and (**B**) (SB) precipitation products against reference data (winter monsoon). conspicuous during winter monsoon period as compared with annual and summer monsoon season. Moreover, all of the SB products showed less range of mean error during annual scale following by summer and winter monsoon seasons. However, the GB products exhibited lower mean error in winter following by annual and summer scale. Overall, the performance of GPCC and TRMM products showed the best agreement with in-situ in terms of higher R\({}^{2}\) values of \(\geq\) 0.90 and lower values of RME \(\leq\) 25% during annual, winter, and summer monsoon seasons. In order to evaluate the temporal pattern variability of different GPPs against observation data, Taylor diagrams were plotted [62] to quantify the precise agreement between the observation and GPPs in terms of correlation coefficients (CC), standard deviation (SD), and root mean square deviation (RMSD), which are shown in Figure 7 as A (GB) and B (SB) precipitation products during annual, winter, and summer seasons. In the diagram, correlation coefficient (CC) is denoted by blue lines adjoining perpendicular to the parabolic scale, standard deviation (SD) is denoted by radii of the black cycles and root mean square deviation (RMSD) donated by the radii of the green cycles. In the diagram, if the value of the GPPs is closer to the observation data, then it is considered to be a better product. The diagram statistics provide the evaluation of the temporal pattern of the GPPs against observed data. Figure 6: Statistical indicators for the assessment of (**A**) (GB) and (**B**) (SB) precipitation products against reference data (summer monsoon). The results indicated that the GPCC and TRMM precipitation products outperformed the other products during the annual scale in terms of higher CC of around 95% and less value of the RMSD.