Is Bias Correction in Dynamical Downscaling Defensible?

Localized projections of 21st-century hydroclimate variables obtained from downscaling Global Climate Model (GCM) output are central to informing regional impact assessments and infrastructure planning. Regional GCM biases can be significant and, for dynamical downscaling, can be addressed either before (a priori) or after (a posteriori) downscaling. However, a priori bias correction (APBC) has generally unexplored effects on climate change signals. Here we analyze dynamically downscaled solutions of CMIP6 GCMs over the Western U.S., with and without APBC, and quantify APBC's impact on climate change signals relative to other irreducible uncertainty sources. For temperature and precipitation, the uncertainty introduced by APBC is negligible compared to that arising from GCM choice or internal variability. Furthermore, APBC greatly reduces regional models' unrealistically high snow-water-equivalent (SWE) biases that result directly from GCM errors. We leverage this finding to encourage the dynamical downscaling community to adopt APBC as a standard operating procedure. Global Climate Models are coarse in resolution and often biased at the regional scale. Thus they are ill-suited to provide local information needed for climate change adaptation planning. Dynamical downscaling is the most physically realistic solution to this problem, and involves running a weather model with climate model boundary conditions. But global model biases in the region of interest can prevent dynamical downscaling from producing data realistic enough for decision-making. To address this challenge, we present the first comprehensive calculations showing that biases in global model boundary conditions can safely be corrected before being used for dynamical downscaling. This finding suggests it is possible to make regional climate projections that are both realistic and dynamically consistent with global model output. A priori bias correction (APBC) of Global Climate Models (GCMs) introduces trivial uncertainty in dynamically downscaled temperature and precipitation projections Corresponding uncertainties in snow are significant, but non-APBC projections of snow are physically unrealistic and should be discarded Minimally invasive APBC preserves GCM trends at regional scales while producing useable and realistic downscaled hydroclimate projections