Validation of historical and future statistically downscaled pseudo-observed surface wind speeds in terms of annual climate indices and daily variability

Surface wind speed variability cannot be resolved by the current generation of Global Climate Models (GCMs) due to their relatively coarse spatial discretization. Downscaling techniques are thus needed to generate finer scale projections of variables like near surface wind speeds. However, classical statistical downscaling experiments are unable to infer which model performs better in a future climate change scenario, as one cannot know the true change in the variable of interest. Additionally, the ability of models to reproduce historical climatologies does not necessarily imply that they will be able to accurately simulate future climate conditions. Moreover, conventional comparisons between downscaling methods have been carried out in terms of standard model performance measures, e.g., correlations and mean squared errors, with infrequent treatment of characteristics such as the ability to reproduce extreme value statistics. To address these limitations, we employ a pseudo-observation downscaling verification approach, which allows one to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from a Regional Climate Model (RCM) nested within the domain of the GCM. The new validation methodology compares historical and future RCM pseudo-observations in terms of both downscaled daily variability and annual climate indices characterized by the proposed Wind INDices for the validation of EXtremes (WINDEX).