Challenges in Multivariate Bias Correction of Convection-Permitting Climate Models for Urban Microclimate Applications

Microclimate models are increasingly used to assess the effectiveness of climate change adaptation strategies against future heat stress. These models require high-resolution climate inputs for multiple variables, including precipitation, air temperature, wind, radiation, and humidity. While the highest spatial and temporal resolution climate information is typically provided by regional climate models, particularly convection-permitting models (CPMs), it remains unclear whether CPM outputs still require bias correction across all relevant variables and whether commonly applied methods such as quantile mapping are suitable in this context. 

In this study, we evaluated the performance of the convection permitting model, COSMO-CLM, against observations for three Swiss cities, Zurich, Geneva, and Lugano, across six climate variables: precipitation, air temperature, solar radiation, wind speed, surface pressure, and relative humidity. Delta quantile-mapping was applied to bias-correct these variables for a historical period (1998–2009) and a future period (2078–2089), using COSMO-CLM simulations driven by MPI-ESM-LR under the RCP8.5 scenario. Model performance was evaluated using cross-validation for the historical period and by comparing the climate change signal of selected climate indices (e.g., Maximum Daily Air Temperature and Annual Mean Precipitation) between raw and bias-corrected outputs for the future period. Additional analyses examined whether inter-variable correlation structures were preserved after bias-correction and whether diurnal temperature patterns were respected. 

The raw COSMO-CLM output exhibits systematic biases across all variables, with particularly pronounced biases in precipitation, temperature, reltaive humidity, and solar radiation. Delta quantile mapping cannnot substantially reducethese biases but can preserve inter-variable correlations.  However, climate change signals that are not explicitly represented in the model were incorporated for wind speed, relative humidity, surface pressure, and solar radiation, while climate change signals for precipitation and temperature are not well preserved. In addition, the method exhibits limitations in representing extreme events especially precipitation events above the 99th percentile and can shift the diurnal air temperature distribution. The latter is of particular concern in this context, as mitigation of heat stress during the hottest hours of the day is the primary focus of climate change adaptation against heat. Variable-specific bias-correction approaches may therefore be required; however, such tailoring can complicate the preservation of physically consistent inter-variable correlation structures. In general, it remainschallenging to identify appropriate evaluation metrics for assessing the usefulness and validity of bias-correction techniques when applied across multiple climate variables. Overall, this study presents a multi-variable assessment of the benefits and limitations of quantile mapping for high-resolution climate data used in urban microclimate modeling and climate change adaptation applications.