Bias Correction of Precipitation from Convection-Permitting Models at the Point Scale: A Case Study in Switzerland

Convection-permitting models (CPMs) outperform coarser regional climate models in resolving convective processes and predicting short-duration, high-impact weather phenomena. Sub-daily rainfall data from CPMs are crucial for impact models, such as urban drainage systems, but bias correction is still needed for the accuracy in local scale. This study estimates biases and uncertainties in CPM simulations at hourly and daily intervals, testing quantile mapping (QM) for bias correction. Data from over 70 weather stations in Switzerland validated CPM outputs and correction methods using sub-hourly rainfall records. COSMO-CLM's 2.2 km simulations were used for historical and future scenarios. Classic QM and several advanced versions, including moving windows and spatial pooling, were tested. Validation focused on three aspects: (A) statistical distribution of precipitation, (B) historical rainfall indices, and (C) future changes in rainfall indices. CPM biases were assessed by (A) and (B), while QM impacts were evaluated by all three. Findings highlight CPM limitations, especially for sub-daily intervals, with wet biases in extreme percentiles up to 30–35 mm/hour. Biases varied with different precipitation patterns. While QM methods reduce biases, they can distort the climate change signal, particularly in hourly rainfall indices. Upgraded QM techniques, such as those using moving windows or spatial pooling, are recommended, though caution is needed with longer observation periods. Future work will examine seasonal precipitation impacts and use diverse CPM ensembles for more reliable simulations.