*4.3. Model Comparison*

ST-CORAbico was compared with the Distribution Transformation method (DT) and the Gamma Quantile Mapping (GQM) method. Using the short- and long-lived storm scenarios that are presented above, Figure 9 presents the spatial differences and linear correlation between the total observed storms and the bias-corrected events obtained by ST-CORAbico, DT, and GQM. The results for both storm event scenarios showed that ST-CORAbico had the lowest spatial difference among the evaluated methods. For the short-lived storm scenario, ST-CORAbico displayed the highest correlation coefficient (*r*: 0.41) and the lowest RMSE and bias (RMSE: 4.05 mm; bias: 0.74) when compared with DT (*r*: 0.40; RMSE: 5.4 mm; bias: 1.17); and, GQM (*r*: 0.39 RMSE: 6.09mm and bias: 1.5). In the case of the long-lived storm, ST-CORAbico and DT showed a notable error reduction in contrast to the GQM method that showed the biggest differences. For this storm scenario, ST-CORAbico had the best performance (*r*: 0.71 RMSE: 18.02 mm; bias: 0.09), followed by DT (*r*: 0.68, RMSE: 23.0 mm; bias: 0.32), and finally GQM (*r*: 0.62, RMSE: 43.77 mm; bias: 0.97).

**Figure 8.** Satellite and bias corrected error distribution for short and long-lived events during monsoon seasons 2014–2017: (**a**,**b**) RMSE; (**c**,**d**) bias; and, (**e**,**f**) correlation coefficient.

**Figure 9.** Comparison between ST-CORAbico vs Distribution Transformation (DT) and Gamma Quantile Mapping (GQM).

Figure 10 presents the comparison between ST-CORAbico, DT, and GQM for short- and long-lived storm events. The boxplots show the distributions of the RMSE (Figure 10a,b), the bias (Figure 10c,d), and the correlation coefficient (Figure 10d,f) between the 25% and 75% percentiles for the original GPM-IMERG and the different bias correction methods. The dots represent the individual error for each storm event. In comparison with the two probabilistic methods, we found that ST-CORAbico consistently had the lowest RMSE as well as the lowest bias for both short- and long-lived storm events. ST-CORAbico and DT had a lower impact on the correlation coefficient, especially for short-lived events.

**Figure 10.** Comparison between the satellite GPM-IMERG (red), ST-CORAbico (blue), Distribution Transformation (green) and Gamma Quantile Mapping (grey) error dispersion during monsoon seasons 2014–2017. (**a**,**b**) RMSE; (**c**,**d**) bias; and, (**e**,**f**) correlation coefficient.
