Spatial interpolation of monthly climate data for Finland: comparing the performance of kriging and generalized additive models

The Finnish Meteorological Institute has calculated statistics for the new reference period of 1981-2010. During this project, the grid size has been reduced from 10 to 1 km, the evaluation of the interpolation has been improved, and comparisons between different methods has been performed. The climate variables of interest were monthly mean temperature and mean precipitation, for which the spatial variability was explained using auxiliary information: mean elevation, sea percentage, and lake percentage. We compared three methods for spatial prediction: kriging with external drift (KED), generalized additive models (GAM), and GAM combined with residual kriging (GK). Every interpolation file now has attached statistical key figures describing the bias and the normality of the prediction error. According to the cross-validation results, GAM was the best method for predicting mean temperatures, with only very small differences relative to the other methods. For mean precipitation, KED produced the most accurate predictions, followed by GK. In both cases, there was notable seasonal variation in the statistical skill scores. For the new reference period and future interpolations, KED was chosen as the primary method due to its robustness and accuracy.