The availability of good and reliable rainfall data is fundamental for most hydrological analyses and for the design and management of water resources systems. However, in practice, precipitation records often suffer from missing data values mainly due to malfunctioning of raingauge for specific time periods. This is an important issue in practical hydrology because it affects the continuity of rainfall data and ultimately influences the results of hydrologic studies which use rainfall as input. Many methods to estimate missing rainfall data have been proposed in literature and, among these, most are based on spatial interpolation algorithms.In this paper different spatial interpolation algorithms have been evaluated to produce a reasonably good continuous dataset bridging the gaps in the historical series. The algorithms used are deterministic methods such as inverse distance weighting, simple linear regression, multiple regression, geographically weighted regression and artificial neural networks, and geostatistical models such as ordinary kriging and residual ordinary kriging. In some of these methods, the elevation information, provided by a Digital Elevation Model, has been added to improve estimation of missing data. These algorithms have been applied to the mean annual and monthly rainfall data of Sicily (Italy), measured at 247 raingauges.Optimization of different settings of the various interpolation methods has been carried out using a subset of the available rainfall dataset (modeling set) while the remaining subset (validation set) has been used to compare the results obtained by the different algorithms.Validation results indicate that the univariate methods, neglecting the information of elevation, are characterized by the largest errors, which decrease when the elevation is taken into account. The ordinary kriging of residuals from linear regression between precipitation and elevation, which has provided the best performance at annual and monthly scale, has been used to complete the precipitation monthly time series in Sicily.