## 3 Results and Discussion  
### Daily Estimates  
Table 2 presents performance assessment of the three methods for filling missing values, in the six testing sites, at the daily time scale (the method with better values for each metric is highlighted in bold). One may notice that, albeit all methods presented relatively low prediction skills, the direct imputation of the IMERG-GPM retrievals led to moderately more accurate results in most cases. In fact, the raw satellite data entailed higher values for the correlation coefficient (0.53, on average) and lower values of RMSE (9.7 mm, on average) in at least four target sites. Exceptions can be made in the Cacheira de Goias and the Campo Alegre gauging stations, in which direct imputation performed worse than the regression-based methods in terms of RMSE, and similarly to SLR with respect to the correlation coefficient. As a matter of fact, the GPM product considerably overestimated the observed annual rainfall (-317 mm) at the former site, which presented the second-lowest observed annual rainfall, but the highest differences with respect to the satellite retrievals, both with respect to the cumulative process and to the observed daily extremes,. On the other hand, the satellite retrievals underestimated the rainfall amounts at the latter site (-217 mm), in which the highest annual precipitation and some of the largest rainfall amounts at the daily scale (>100 mm) were observed. We note that both sites are located in regions with weak topographic gradients, which should, at least to some extent, exclude the influence of terrain complexity in the relatively poor performance of the satellite product [33]. However, some influence of long-term climate conditions and of the variability of rainfall extremes [34; 45] is apparently perceived in these sites. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CC**} & \multicolumn{3}{c}{**MAE (mm)**} & \multicolumn{3}{c}{**RMSE (mm)**} & \multicolumn{3}{c}{**PBias (\%)**} \\ \cline{2-13}  & **MLR** & **SLR** & **GPM** & **MLR** & **SLR** & **GPM** & **MLR** & **SLR** & **GPM** & **MLR** & **SLR** & **GPM** \\ \hline Cacheira de Goias & **0.31** & 0.20 & 0.22 & 3.72 & **3.45** & 4.92 & **7.39** & 7.77 & 11.06 & **14.20** & \(-\)49.70 & 27.80 \\ Campo Alegre & 0.29 & **0.47** & 0.44 & 5.37 & **4.70** & 5.46 & 14.61 & **14.07** & 14.27 & \(-\)9.1 & \(-\)67.80 & \(\mathbf{-12.00}\) \\ Corrego do Ouro & 0.44 & 0.35 & **0.60** & 4.41 & **4.08** & 4.26 & 10.55 & 11.16 & **9.45** & \(-\)15.2 & \(-\)63.20 & **2.10** \\ Crominia & 0.49 & 0.40 & **0.62** & 4.27 & 4.25 & **3.66** & 9.77 & 10.49 & **8.98** & 1.3 & \(-\)48.00 & \(\mathbf{-6.00}\) \\ Montividiu & 0.39 & 0.43 & **0.60** & 3.59 & 3.30 & **3.19** & 7.81 & 7.66 & **7.37** & 16.90 & \(-\)40.70 & **18.60** \\ Quirinópolis & 0.46 & 0.42 & **0.72** & 4.00 & 3.83 & **3.17** & 8.98 & 9.35 & **7.12** & \(-\)15.10 & \(-\)62.80 & \(\mathbf{-6.80}\) \\ \hline \hline \end{tabular} Note: Best methods for filling missing values are indicated in bold. CC, Correlation coefficient; MAE, mean absolute error; RMSE, root mean square error; MLR, multiple linear regression; SLR, simple linear regression; GPM, Global Precipitation Measurement method.  
\end{table} 
Table 2: Performance indexes of the three methods for filling daily missing data at the target sites. It is also worth noting that the GPM product performed similarly, in terms of MAE (4.1 mm, on average), to the MLR models (4.0 mm, on average). However, since the former presented lower values of RMSE, one may infer that larger rainfall amounts are more accurately reproduced by the satellite retrievals. As for PBias (4%, on average), two gauging stations, namely, Cachoeira de Goias, and Montividiu, presented strong tendencies of overestimation, whereas the remaining gauges were less biased, with prevailing behavior towards negative values.