Statistical downscaling methods have been actively developed to improve the applicability of global climate models’ (GCMs) outputs to impact studies at a local scale. This study presents a multivariate bias correction (MBC) method that preserves GCM-driven climate change signals and the interdependence between hydro-climate variables by combining the quantile delta mapping (QDM) method with distribution-free shuffle approach, refer to as MBCDS. Since MBCDS also employs the Gaussian rank correlation directly, it does not require an iterative process to improve the Gaussianity as used in existing MBC methods. This study evaluated the effects of interdependence on univariate- and multivariate-distribution criteria and hydrologic indicators during a historical baseline period. Furthermore, this study examined potential changes under selected CMIP5 climate projections for the three future time windows. Application of the downscaling outputs to hydrologic simulations showed that the univariate method underestimated snowfall considerably during the snowfall period, which leads to less snow accumulation and subsequently resulting in less spring and summer flows. On the contrary, the multivariate methods improved all of multivariate-distribution criteria, hydrologic indicators, and the ability in reproducing snowfall depending on multiple hydro-climate variables (i.e., precipitation and temperature). Moreover, the univariate method projected less increase in winter snowfall compared to those of multivariate methods, indicating that the univariate method may result in biased future conditions. In particular, MBCDS showed better and comparative performance compared to the existing methods with regard to climatic and hydrologic performance measures and considerably improved the computational time, with up to 53% less computing time required than those of other existing methods which include an iterative process. Therefore, this study proved that MBCDS can be a good alternative to provide high-resolution climate projections for impact studies at local scales. Further, the study also demonstrated that consideration of the interdependence between hydro-climate variables using multivariate methods is essential to avoid erroneous assessment of climate change impacts for snow-dominated watersheds such as those in Alberta, Canada.
