Bias Correcting Regional Scale Earth Systems Model Projections: Novel Approach using Empirical Mode Decomposition

Abstract. Bias correction is a crucial step in using Earth systems model outputs for assessments, as it adjusts systematic errors by comparing the model to observations. However, standard methods – ranging from mean-based linear scaling to distribution-based quantile mapping typically treat bias correction as a single-scale process, overlooking the fact that biases can manifest differently across daily, seasonal, and annual timescales. In this study, we propose a novel, timescale-aware bias-correction approach built on Empirical Mode Decomposition (EMD). By decomposing the meteorological signal into multiple oscillatory components and aggregating them to represent distinct timescales, we apply targeted corrections to each component, thereby preserving both short- and long-term structure in the data. Experimental validations demonstrate that this finer-grained method substantially improves upon existing bias-correction techniques such as quantile mapping. As a result, the proposed approach offers a more robust path to accurate and reliable Earth systems projections, strengthening their utility for resilience and adaptation planning.