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The subseasonal predictability of heatwaves in Europe is relatively well understood regarding prediction skill horizon and physical drivers of predictability. Despite this progress, few studies have translated subseasonal model output into skillful operational heatwave forecast products and end-user-tailored impact forecasts. These are substantial challenges, given the relatively high uncertainties and the flow-dependent skill inherent in subseasonal prediction. In this project, we aim to translate subseasonal model output from the European Centre for Medium-Range Weather Forecasts (ECMWF) into end-user-tailored heatwave forecast products for Switzerland. We first perform a detailed verification of average subseasonal (hindcast) prediction skill for temperature and heatwaves in Switzerland on different spatial and temporal aggregation scales. This analysis demonstrates a significant increase in subseasonal forecast skill with increasing temporal aggregation scales. We then analyze to what extent previously-studied local and remote drivers (such as dry soils, lower-frequency atmospheric modes, or sea surface temperature anomalies) manifest as windows of forecast opportunity for heatwave prediction in Switzerland and its subregions. These steps are performed with two sets of hindcasts one with the native grid resolution and one that has been downscaled (and bias-corrected) to a higher resolution using quantile mapping. This postprocessing helps to quantify the added value of downscaling at subseasonal lead times. Finally, we present some ideas on how the gained knowledge on spatio-temporal and flow-dependent characteristics of skill could be translated into an operational subseasonal heatwave prediction system for Switzerland a step that is closely linked to the challenging question of how much skill is enough skill for specific end-user applications.
Büeler et al. (Fri,) studied this question.