Understanding the Cascade: Removing GCM Biases Improves Dynamically Downscaled Climate Projections

Polarization surrounding bias correction (BC) in creating climate projections arises from its lack of physicality. Here, we perform and analyze 18 dynamical downscaling simulations (with and without BC) to better understand the physical impacts of BC, applied before downscaling, on regional climate output across the western United States. Without BC, downscaled precipitation is systematically and unrealistically wet biased compared to a hierarchy of observationally based datasets over the 1980–2014 period due to cascading mean‐state Global Climate Model (GCM) biases: (a) overly strong lower‐tropospheric lapse rates (5 K/km), (b) overly cold (2 K) tropospheric temperatures, and (c) anomalous mid‐tropospheric cyclonic vorticity advection. With BC, downscaled precipitation (snow) biases are virtually eliminated (halved). Identified GCM biases are common to the broader Coupled Model Intercomparison Project ensemble. Physical effects of BC on the quality of the regionalized projections, pending an evaluation of BC's distortion of the downscaled climate response, may motivate its broader application by dynamical downscalers. Plain Language Summary Global Climate Models (GCMs) are known to have biases that, when dynamically downscaled, damage the credibility of the e. A longstanding way around this problem is bias correction (BC) after downscaling, but this practice rarely involves physics and can mislead climate data users into overvaluing the quality of the downscaled data. Further, post‐downscaling BC techniques can over correct the higher‐order statistics, calling into question the faithful preservation of the original simulated signals. For the first time, we apply a minimally invasive BC procedure to a group of 9 GCMs in order to define physical relationships between mean GCM biases and their dynamically downscaled hydroclimate variables across the western United States. We find that native GCMs tend to exhibit surprisingly common mean biases that, when downscaled, effectuate an overly wet, cold, and snowy climate across the region. Key Points Bias correction of Global Climate Models (GCMs) reduces biases in downscaled mean precipitation, snow, and temperature across the western United States Cascading cold, thermodynamically unstable, and cyclonic vorticity biases from GCMs to regional climate models drive wet biases in dynamical downscaling CMIP6‐wide GCM biases are similar suggesting that biases in dynamically downscaled precipitation and temperature can