Abstract: This work analyses three uncertainty sources affecting the observation-basedgridded data sets: station density, interpolation methodology and spatial resolution.For this purpose, we consider precipitation in two countries, Poland and Spain,three resolutions (0.11, 0.22 and 0.44), three interpolation methods, both areal-and point-representative implementations, and three different densities of theunderlying station network (high/medium/low density). As a result, for each resolu-tion and interpolation approach, nine different grids have been obtained for eachcountry and inter-compared using a variance decomposition methodology.Results indicate larger differences among the data sets for Spain than for Poland,mainly due to the larger spatial variability and complex orography of the formerregion. The variance decomposition points out to station density as the most influ-ential factor, independent of the season, the areal- or point-representative imple-mentation and the country considered, and slightly increasing with the spatialresolution. In contrast, the decomposition is stable when extreme precipitation indi-ces are considered, in particular for the 50-year return value.Finally, the uncertainty due to station sub-sampling inside a particular grid boxdecreases with the number of stations used in the averaging/interpolation. In thecase of spatially homogeneous grid boxes, the interpolation approach obtains simi-lar results for all the parameters, excepting the wet day frequency, independently ofthe number of stations. When there is a more significant internal variability in thegrid box, the interpolation is more sensitive to the number of stations, pointing outto a minimum stations?density for the target resolution (six to seven stations).

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