## 4 Conclusions  
This paper discussed the use of statistical-based models and the direct imputation of retrievals from the IMERG-GPM product for filling missing rainfall data in gauging stations located in the Brazilian midwestern region, at daily and monthly time scales. For this, we derived multiple linear regression (MLR) models with fixed \"donor\" sites and simple linear regression (SLR) models with variable \"donor\" stations, depending on data availability on the best \"donors\", and compared the performance of the three methods, through a set of metrics, by replacing the water year of 2016-2017 with the correspondent estimates. At the daily time scale, the direct imputation of satellite retrievals provided more accurate results than the statistical-based methods, and described, in a more effective manner, the alternation of dry and wet states of rainfall along the period of evaluation. In effect, the regression models could not capture the spatial variability of complex rainfall fields, which disrupted empirical probability dry at the target sites, and were also unable to reproduce the largest rainfall amounts, being apparently bounded in the range 20-30 mm/day. In addition, at least for this case study, the use of more complex MLR models did not seem justified, as their performance in the goodness-of-fit assessment was similar to those of the more parsimonious SLR models. However, the satellite estimates were biased, mainly with respect to more extreme rainfall events, which might strongly impact frequency analysis expedients. As a result, the adoption of bias-correction procedures (e.g., [PERSON] et al. (2021) [33]) prior to imputation might improve estimation of missing values; this is envisaged as our next research development. Moreover, since the performance of satellite products is inherently dependent on climate and topography [44; 54], our results cannot be readily generalized to other regions, although we believe that, at least for time evolution of dry and wet states, remote-sensing data can provide more reliable predictions than the use of \"donor\" sites. This, however, still calls for further investigation and will be addressed in future work. On the other hand, the aggregation of the process for deriving monthly data smoothed out the effects of local extremes and intermittence conditions and, as a result, the performance of the statistical-based methods, at least with respect to the metrics utilized in this study, is comparable to those obtained with the direct imputation of satellite retrievals. However, the seasonal precipitation patterns and the month-to-month variability of rainfall amounts were not properly described by the former. This problem is, at least to some extent, attenuated by using remote-sensing data, albeit the IMERG-GPM product could not perform well in all test sites. Moreover, strong tendencies of overestimation were verified in some of the test sites, which reinforces our conjecture that utilizing a bias-correction procedure might improve results, albeit more complex models would result in this case. To sum up, our results suggest that the imputation of satellite retrievals for filling missing data at times scales ranging from day to month is a feasible alternative and might outperform well-established statistical methods when one is dealing with complex rainfall fields and marked seasonality in precipitation patterns. Of course, the satellite datasets did not span long periods of record, which would certainly limit their use, and more case studies, under a broader variety of climate and terrain complexity conditions, are necessary for providing more general conclusions. However, we believe that the proposed framework may comprise an appealing tool for researchers and practitioners for dealing with missing data in rainfall time series. [PERSON] and [PERSON] wrote the paper and analyzed the data and the results; [PERSON] revised the manuscript and analyzed the results. All authors have read and agreed to the published version of the manuscript.