State-of-the-art bias correction of climate models misrepresent climate science and misinform adaptation

Abstract

Quantile Mapping (QM) based Bias Correction and Spatial Disaggregation (BCSD) have emerged as the de facto standard for rectifying bias and scale-mismatch in global climate models (GCMs) leading to novel climate science insights and new information for impacts and adaptation. Focusing on critical variables crucial for understanding climate dynamics in India and the United States, our evaluation challenges the premise of BCSD approach. We find that BCSD overcorrects GCM simulations to observed patterns while minimizing or even nullifying science-informed projections generated by GCMs. Furthermore, we show that BCSD incorrectly captures extremes and complex climate signals. Our evaluation in the context of the Walker Circulation suggests that this inability to adequately capture multivariate and spatial-temporal dependence patterns may at least partially explain the challenges with BCSD.