Correction of Precipitation Bias from Convection-Permitting Models at the Station Scale in Switzerland

Unlike regional climate models, convection-permitting models (CPMs) are able to resolve convection-scale processes and therefore better estimate short-duration, extreme precipitation events, particularly useful for the urban drainage community. Despite their state-of-the-art capabilities, bias correction of CPMs is still required to ensure their output is representative of the station scale, a resolution needed by many urban drainage models. Due to its simplicity, quantile-mapping is commonly used for bias-correction and downscaling, but does come with limitations that have not yet been evaluated for CPMs. This study tests five variations of empirical quantile-mapping to bias-correct and downscale the 2.2 km simulations of COSMO-CLM (a CPM) for over 70 weather stations in Switzerland. Ten years of simulation data are corrected using ten years of observations at the 30-minute interval. Traditional QM and several advanced versions are evaluated, including: using a 91-day moving window to account for temporal variability, spatial pooling of surrounding grid cells for spatial variability, and extending the observational record (to 30 years) for data variability. These techniques are validated using cross-validation and through evaluation of historical rainfall indices (e.g., consecutive dry days) and the climate change signal. Findings show that wet biases in raw CPM output remain (up to 30-35 mm/hour above the 98^th quantile) and only the moving window technique (and its combination with spatial pooling) is able to reduce biases in quantiles above the 98^th. All QM methods do reduce remaining biases, but can distort the climate change signal, particularly in indices related to frequency of rainfall. Despite the additional computational burden, the moving window technique is highly recommended to the urban drainage community as a robust technique for CPM downscaling. As more CPM simulations become available, future work will reexamine these aspects for a range of CPMs, time periods, and simulation domains.