Exploring quantile mapping as a tool to produce user tailored climate scenarios for Switzerland

In the context of the upcoming CH2018 Swiss climate scenarios, empirical quantile mapping (QM) is employed to bias-correct and to downscale raw climate model output enabling the production of user-tailored data products that are directly applicable in climate impact research. We here present the overall CH2018 QM approach as well as exemplary results. The approach consists of three different QM setups that target three different spatial scales. All setups produce transient time series (1981-2099) of several meteorological variables at daily resolution and for all climate model chains considered in CH2018.