The derivation of local scale information from integrations of coarse-resolution General Circulation Models (GCM) with the help of statistical models fitted to present observations is generally referred to as statistical downscaling. In thi s paper a relatively simple analog method is described and applied for downscal ing purposes. According to this method the large-scale circulation simulated by a GCM is associated to the local variables observed simultaneously with the most similar large-scale circulation pattern in a pool of historical observ ations. The similarity of the large-scale circulation patterns is defined in ter ms of their coordinates in the space spanned by the leading observed Empirical O rthogonal Functions (EOFs). The method can be checked by replicating the evolution of the local variables in an independent period. Its performance for monthly and daily winter rainfall in the Iberian peninsula is compared to more complicated techniques, each belongi ng to one of the broad families of existing statistical downscaling techniques: a method based on Canonical Correlation Analysis (CCA) as representative of line ar methods; a method based on Classification and Regression Trees (CART) as repr esentative of a weather generator based on classification methods; and a neural network, as an example of deterministic non-linear methods. It is found in these applications that the analog method performs in general as well as the more complicated methods, and it can be applied to both normally and non-normally distributed local variables. Furthermore, it produces the right le vel of variability of the local variable and preserves the spatial covariance be tween local variables. On the other hand linear multivariate methods offer a cle arer physical interpretation that supports more strongly its validity in an alte red climate. Classification and neural networks are generally more complicated m ethods and do not directly offer a physical interpretation.