Statistical and dynamical downscaling methods are tested and compared for downscaling seasonal precipitation forecasts over Spain from two DEMETER models: the European Centre for Medium-Range Weather Forecasts (ECMWF) and the UK Meteorological Office (UKMO). The statistical method considered is a particular implementation of the standard analogue technique, based on close neighbours of the predicted atmospheric geopotential and humidity fields. Dynamical downscaling is performed using the Rossby Centre Climate Atmospheric model, which has been nested to the ECMWF model output, and run in climate mode for six months. We first check the performance of the direct output models in the period 1986–1997 and compare it with the results obtained applying the analogue method. We have found that the direct outputs underestimate the precipitation amount and that the statistical downscaling method improves the results as the skill of the direct forecast increases. The highest skills – relative operating characteristic skill areas (RSAs) above 0.6 – are associated with early and late spring, summer and autumn seasons at zero- and one-month lead times. On the other hand, models have poor skill during winter with the exception of the El Niño period (1986–1988), especially in the south of Spain. In this case, high RSAs and economic values have been found. We also compare statistical and dynamical downscaling during four seasons, obtaining no concluding result. Both methods outperform direct output from DEMETER models, but depending on the season and on the region of Spain one method is better than the other. Moreover, we have seen that dynamical and statistical methods can be used in combination, yielding the best skill scores in some cases of the study.