This paper investigates the effect of a Bayesian Model Averaging (BMA) method on simulated precipitation over the Columbia River Basin using two statistically downscaled climate datasets, i.e., Bias-Correction Spatial Disaggregation (BCSD) and Multivariate Adaptive Constructed Analogs (MACA). To this end daily observed and simulated precipitation are used to calculate different indices focusing solely on seasonality, event-timing, and variability in timing (persistence) of the precipitation. The climate model weights are estimated for each cell (6x6 km) of the Columbia River Basin using daily time series for the historical period 1970-1999 from the ten Global Climate Models (GCMs) participating to the Coupled Model Intercomparison Project platform, Phase 5. The results show that BMA results in more than 15 % improvement/reduction in mean absolute error as compared to the individual GCMs. The improvement is, in general, higher for the MACA models than for the BCSD models. The results of variability in precipitation timing show that extreme precipitation events are mostly not persistent (i.e., occurring in different periods throughout the year), i.e., more than 75 % of the grid cells with an elevation above 900 m indicate persistence values less than 0.2 whereas nearly 70 % of the high elevation cells indicate such low persistence. Further we find that the variability in persistence is higher in high elevation cells than those with low elevation. The picture is different for MACA ensembles as the simulated persistence of extreme precipitation events is higher than that for the observed and BCSD datasets.