## 3 Results and Discussion
### Daily Estimates
As previously stated, the satellite product could not reproduce the observed rainfall in the Cachoeira de Goias gauging station in overall terms, which may justify the large bias at this site. However, at the Montividiu gauging station, the other metrics indicated the best performance of the satellite retrievals among the evaluated methods. We note that the GPM product also overestimated the annual rainfall amounts in Montividiu but, in this case, the differences in daily extremes were not too marked. Hence, we believe that the positive bias may have stemmed from a large number of false alarms by the satellite product, i.e., relatively small rainfall amounts captured by the remote sensing equipment but not by the gauge. Overall, the worst performance appears to be related to SLR, with stronger tendencies of underestimation (\(-55\%\), on average) and larger values of MAE (4.2 mm, on average) and RMSE (10.1 mm, on average) in virtually all testing sites. This might be ascribed to the low values of the regression coefficients \(\beta\), which spanned from about 0.20 to 0.5. This fact indicates that the estimates from the SLR equations would consistently lie above the equality line (when plotting the observed values in the y axis). Moreover, the models were able to simulate, at most, approximately half of the rainfall amounts recorded at the \"donor\" sites, which may justify the strong underestimation tendency observed at the target counterparts. Finally, the correlation levels were, in general, low for this method (0.38, on average), which may suggest that, in addition to underestimated rainfall amounts, the temporal dynamics of the observed precipitation were not properly captured by a single \"donor\" site - the usually complex spatial distribution of dry spells and extreme events, which may present strong distinctions from site to site on a given day, might effectively play a large role in the inaccurate estimates obtained with SLR at the target sites. The MLR models, in turn, presented an intermediate performance among the other techniques. On the one hand, despite the lowest values of MAE among the three methods being evaluated, the predictive skills of the MLR equations, as indicated by the values of CC (0.40, on average), were, in general terms, only slightly better than the SLR counterparts. As with the SLR models, the values of the regression coefficients were typically low, ranging from approximately 0.10 to 0.35, which should again place most rainfall estimates above the 1:1 line (when plotting the observed values in the y axis). Moreover, the relatively low values of CC may indicate some disruption of the temporal dynamics of the observed rainfall. On the other hand, the MLR equations resulted in considerably lower levels of systematic bias (\(-1.17\%\), on average), although this might be a spurious effect of the rainfall estimates not being bounded from below by zero, but instead by the intercepts of the regression equations that ranged from approximately 1.5 mm to 2.8 mm. Finally, the values of RMSE were slightly larger than those obtained with the satellite retrievals, which indicates a poorer reproduction of the higher order statistics. As a summary, we note that even the direct imputation has not entailed high levels of linear correlation, which may indicate that, at least to some extent, the downscaling procedure has disrupted the time evolution of dry and wet states along the observed time series, as well as introduced bias to rainfall estimates. Hence, a bias-correction procedure prior to imputation might be beneficial for practical purposes [36]. Furthermore, the results in Table 2 suggest that the increased complexity of the MLR models, with respect to the SLR counterparts, does not seem justified, as the inclusion of a larger number of predictors (i.e., \"donor\" sites) has not resulted in noticeable better performances of the former.