Added value of a priori bias correcting dynamically downscaled data for application to species distribution models - a case study for coastal British Columbia

Predicting changes in species distributions under climate change relies on high-quality climate projections. In this case study of coastal British Columbia, we prepare and evaluate two sets of climate data - a priori bias corrected and non bias corrected dynamically downscaled historical projections of Community Earth System Model 2 simulations. We compare these datasets with downscaled ERA5 reanalysis focusing on commonly used inputs to species distribution models (SDM), namely, bioclimatic (BIOCLIM) variables and climate extreme indices. Our results show improvements for mean BIOCLIM variables when a priori bias correction is applied. However, modest improvements are observed in terms of variability and extreme indices. Overall, our findings suggest that a priori bias corrected dynamically downscaled climate projections provide more accurate input to SDMs, and thus can improve the reliability of these important ecological models.