Biodiversity will probably be threatened by climate change effects and the Mediterranean area is a well know hotspot of genetic diversity. Climatic data are a very important source of information for those studies and the aim of this work was to study and compare eight methods for spatial interpolation of climatic data and indices including parametric and non-parametric methods, deterministic, regressive and geostatistical. The Root Mean Square Error (RMSE), relative RMSE (rRMSE) and relative BIAS (rBIAS) were calculated to assess algorithm’s performances in a Mediterranean region. None of the eight methods performed much better than others with a very complex physiographic environment. The range of errors was very high and rRMSE varied from 3.8% to 295%. Anyway, even in case of low differences among methods and despite the necessity of the assumption of normality of data, the interpolation at local scale with parametric and geostatistical methods (e.g. kriging or cokriging) should be preferred to globally-interpolated climatic data due to the possibility to obtain the distribution of prediction’s error. © 2017, Patron Editore S.r.l. All rights reserved.