###### Abstract
However, according to [21], due to the marked spatial variability and intermittence of the rainfall process at fine time scales, such approaches are more recommended for filling monthly and annual rainfall amounts; the aggregation process smooths out the higher-resolution time series, which attenuates the effects of small timescale extremes and dry spells and might strongly reduce estimation errors [11]. On the other hand, daily and sub-daily precipitation information are basic inputs for hydrological models and rainfall frequency analysis, but, in general, models for filling missing values at such small timescales are scarce and affected by low predictive abilities due to the high levels of variability of the process [22; 23]. As a means of tackling this problem, it is usual to discard those years (or other time windows) in which missing values occur. This, however, might result in very short samples or hinder the proper description of the upper tail of the random variable distribution [24]. Alternatively, one might resort to regression-based techniques for estimating the missing values when the modeling errors are acceptable, but this is an unusual situation for daily data. Previous research has discussed the use of multiple linear regression [25], simple substitution [14], the Theil method [26], and machine-learning algorithms [27; 28] for this purpose, but the effectiveness of these methods is still inherently dependent on the ability of the gauging network to reproduce the observed rainfall fields, particularly those with complex spatial patterns, and the more extreme events. As a result, the filling of missing rainfall data at the daily (or shorter timescale) remains an open research field [24; 29]. In recent years, the use of precipitation estimates retrieved from remote-sensing data sources, such as satellites, has gained popularity among researchers and practitioners around the world, especially in regions with poor density gauging [30]. In fact, satellites provide products with high sampling frequencies, improved spatial resolution at large catchments or regions, not corrupted by missing data, and with low costs for the end-users [31]. On the other hand, due to the limitations of the retrieval algorithms, satellite estimates are frequently biased, even after time-averaging [32], which might hinder their utilization without pre-processing or bias correction [33]. Yet, the levels of bias may considerably vary for distinct intervals of rainfall amounts (e.g., [34]) and, at least for some ranges, the satellite retrievals may reasonably agree with ground-based information. This fact has prompted the research question as to whether utilizing satellite rainfall estimates for filling missing data in rainfall gauging stations enclosed by a satellite pixel comprises a more effective and accurate alternative than using data from nearby sites. Recent papers have addressed this rationale for filling missing rainfall values at large time scales, such as monthly or the seasonal ones [35; 36; 37]. However, to the best of our knowledge, the use of satellite retrieval for filling daily rainfall data has not been fully explored in the literature. To address this potential research gap, the main objective of this study is assessing the feasibility of direct imputation of remote sensing data provided by Global Precipitation Measurement (GPM) satellites, via the Integrated Multi-satellite Retrievals for GPM (IMERG) algorithm, for filling the missing data in rain gauges. For this, we evaluated whether this framework yielded better results, at daily and monthly time scales, as compared to traditional regression-based methods, namely, multiple linear regression (MLR) with fixed \"donor\" sites, and simple linear regression (SLR), which utilizes a single \"donor\" site that may otherwise vary due to information availability in nearby stations, in a collection of rain gauges in the midwestern Brazilian region. The remainder of the paper is organized as follows.