We compare versions of six interpolation methods for the interpolation of daily precipitation, mean, minimum and maximum temperature, and sea level pressure from station data over Europe from 1961 to 1990. The interpolation methods evaluated are global and local kriging, two versions of angular distance weighting, natural neighbor interpolation, regression, 2D and 3D thin plate splines, and conditional interpolation. We first evaluated, using station cross-validation and several skill scores, relative skill of each method at estimating point values, looking at spatial and temporal patterns and the frequency distribution of the variables. We then compared, for precipitation, gridded area averages from the candidate interpolation methods against existing high-resolution gridded data sets for the UK and the Alps, which are derived from a much denser network of stations. In both point and area-average cases, differences in skill between interpolation methods at any one point are smaller than the range in skill for a single method either across the domain, or in different seasons. The main control on spatial patterns of interpolation skill is density of the station network, with topographic complexity a compounding factor. The relative skill of different methods remains relatively constant through time, despite a varying station network. Skill in interpolating extreme events is lower than for average days, but relative skill of different methods remains the same. We select global kriging as the best performing method overall, for use in the development of a daily, high-resolution, long-term, European data set of climate variables as part of the EU funded ENSEMBLES project. Copyright 2008 by the American Geophysical Union.
