The goal of this work is to develop a general methodology to obtain joint projections of climate indexes, based on ensembles of global climate model (GCM) output and historical records. As a case study, we consider sea surface temperature (SST) in the North Pacific Ocean. We use two ensembles of 17 different GCM results, made available in the fourth assessment report of the Intergovernmental Panel on Climate Change: one corresponds to 20th-century forcing conditions, and the other corresponds to the A1B emission scenario for the 21st century. Given a representation of the SST spatio-temporal fields based on a common set of empirical orthogonal functions, we use a hierarchical Bayesian model for the empirical orthogonal function coefficients to estimate a baseline and a set of model discrepancies. These components are all time varying. The model enables us to extract relevant temporal patterns of variability from both the observations and the simulations and obtain common patterns from all 18 series. This is used to obtain unified 21st-century forecasts of relevant oceanic indexes as well as whole fields of North Pacific SST forecast. We compare the forecast index for different timescales and compare the SST reconstructions with the GCMs for the 21st century. Although the coarser time resolution produces clearer and faster results, we show that finer timescales produce results with structures that are similar to ones obtained at coarser scales. Copyright © 2012 John Wiley & Sons, Ltd.