A set of different methods are used for climate zones determination, typically a combination of Principal Component Analysis (PCA) and Cluster Analysis (CA). Another method is the Functional Clustering (FC) which consists of: 1) converting observations gathered at discrete time into functional data by means of a system of basis and 2) using the coefficients of the time series expansion for the final classification since each time series is representative of location climate variability. A comparison of different methods is proposed in order to delineate guidelines for the choice of proper clustering method according to the goal of the analysis. Furthermore, we make gridded dataset of Italian climate zones obtained by FC publicly available.