In this study, dynamical and statistical downscaling methods for estimating seasonal statistics of significant wave heights (SWH) were intercompared, with the downscaling results being evaluated against the ERA40 wave data in terms of climatological characteristics and interannual variability. It was also shown that biases in climate‐model‐simulated climate and variability of the atmospheric circulation (or predictors in general) can result in large biases in the estimated climate and variability of SWH (or the predictand in general), and that such biases can be effectively diminished by using standardized predictor quantities in statistical downscaling models. In dynamical downscaling, however, model variability biases remain to be dealt with, whereas the effects of model climate biases can be reduced to some extent by replacing the climate‐model‐simulated wind climate with the observed one. Therefore, the dynamical approach was found to be not as good as the statistical methods in terms of reproducing the observed climate and interannual variability of the predictand, although it bears substantial similarity to the statistical methods in terms of projected possible future changes. Also, it was shown that the observed interannual variability of seasonal statistics (including extremes) can be better reproduced by using 12‐hourly, rather than seasonal, data in statistical downscaling. This stresses the importance of availability of higher‐resolution data from climate model outputs. Nevertheless, a non‐stationary extreme value model with covariates was found to be the best in reproducing the observed climate of extremes. All the statistical downscaling methods and the intercomparison results are applicable to other climate variables (not limited to ocean wave heights). Copyright © 2009 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.