This is the second in a pair of papers in which the performance of Statistical Downscaling Methods (SDMs) is critically re-assessed with respect to their robust applicability in climate change studies. Whereas Part I focused on temperatures (Gutiérrez et al. 2013), the present manuscript deals with precipitation and considers an ensemble of twelve SDMs from the analog, weather typing and regression families. First, the performance of the methods is cross-validated considering reanalysis predictors, screening different geographical domains and predictor sets. Standard accuracy and distributional similarity scores, and a test for extrapolation capability are considered. The results are highly dependent on the predictor sets, with optimum configurations including information from middle tropospheric humidity. Second, a reduced ensemble of good performing SDMs is applied to four GCMs to properly assess the uncertainty of downscaled future climate projections. The results are compared with an ensemble of Regional Climate Models (RCMs) produced in the ENSEMBLES project. Generally, the mean signal is similar with both methodologies (with the exception of Summer, which is drier for the RCMs) but the uncertainty (spread) is larger for the SDM ensemble. Finally, the spread contribution of the GCM and SDM-derived components is assessed using a simple analysis of variance previously applied to the RCMs, obtaining larger interaction terms. Results show that the main contributor to the spread is the choice of the GCM, although the SDM dominates the uncertainty in some cases during Autumn and Summer due to the diverging projections from different families.