###### Abstract  
Accurate estimation of precipitation patterns is essential for the modeling of hydrological systems and for the planning and management of water resources. However, rainfall time series, as obtained from traditional rain gauges, are frequently corrupted by missing values that might hinder frequency analysis, hydrological and environmental modeling, and meteorological drought monitoring. In this paper, we evaluated three techniques for filling missing values at daily and monthly time scales, namely, simple linear regression, multiple linear regression, and the direct imputation of satellite retrievals from the Global Precipitation Measurement (GPM) mission, in rainfall gauging stations located in the Brazilian midwestern region. Our results indicated that, despite the relatively low predictive skills of the models at the daily scale, the satellite retrievals provided moderately more accurate estimates, with better representations of the temporal dynamics of the dry and wet states and of the largest observed rainfall events in most testing sites in comparison to the statistical models. At the monthly scale, the performance of the three methods was similar, but the regression-based models were unable to reproduce the seasonal characteristics of the precipitation records, which, at least to some extent, were circumvented by the satellite products. As such, the satellite retrievals might comprise a useful alternative for dealing with missing values in rainfall time series, especially in those regions with complex spatial precipitation patterns. f 202  
respect to the density of the gauging network and the periods of record of the available gauges, which may hinder the proper characterization of the rainfall stochastic process at the locations of interest [9; 10]. Another issue frequently verified in ground-based rainfall time series is the existence of missing values, i.e., points in time in which no measurement is performed. Missing data in rainfall time series may also strongly affect statistical analysis, both with respect to the probabilities of wet and dry states of the process and to the description of its extremes [11]. In effect, the existence of missing values during wet spells (at shorter time scales) might conceal the largest rainfall amounts during a given time window (e.g., a water year). This may entail the loss of useful information for describing the decay of the upper tails of the parent or the block-maxima distributions of precipitation volumes for a given duration and increase the effects of sampling errors in parameter and quantile estimates. On the other hand, long periods of missing data during dry seasons (or larger time scales) may affect meteorological drought assessment, regarding the mean and maximum duration and severity of extreme events [12]. In a more general context, missing values may compromise the reliability of stochastic simulations of the rainfall process (e.g., [11; 13]), as well as rainfall-runoff modeling expedients. Thus, pre-processing of the raw data set for filling missing values in the rainfall time series is a frequently required in hydrological studies [14]. Methods for filling missing rainfall values are designed for constructing complete data sets by imputing model-based rainfall estimates to the corrupted time series. The most common techniques for this purpose are the regional weighting method, linear regression models, and regional vectors [15]. Additionally, the gauge mean estimator [16], the inverse distance weighting method [17], and ordinary Kriging [18; 19; 20] have been increasingly used because of their simplicity and ease of application. Although very distinct in their mathematical formalism, these methods are based on a similar rationale: information recorded at nearby sites is utilized for estimating the missing values at the target location by means of an \"averaging\" process.