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Global climate models (GCMs) are the primary tools to simulate multi-decadal climate dynamics and to generate global climate change projections under different future emission scenarios (Taylor, Stouffer, & Meehl, 2011). However, these models have a coarse resolution (typically a few hundred kilometres) and suffer from substantial systematic biases when compared with observations (Flato et al., 2013, section 9.6). Therefore, they are unable to provide actionable information at the regional and local spatial scales required in impact and adaptation studies. In order to bridge this gap, two main downscaling approaches have been developed since the early 1990s (Leung, Mearns, Giorgi, & Wilby, 2003; Maraun et al., 2010): dynamical downscaling (based on regional climate models [RCMs]) and empirical/statistical downscaling (ESD, based on statistical models). The relative merits and limitations of both dynamical and statistical downscaling—and combinations of them—have been widely discussed in the literature (see, eg, Fowler,