Spatiotemporally Consistent Multivariate Bias Correction for Climate Projections via Nested Vine Copulas

TL;DR

This paper introduces GN-VBC, a novel multivariate bias correction method for climate models that preserves spatiotemporal and inter-variable dependencies using nested vine copulas and GAMs.

Contribution

It proposes a hierarchical dependence modeling approach combining nested vine copulas with GAMs to improve bias correction in climate projections.

Findings

Improved preservation of inter-variable and spatial dependence in climate data.

Enhanced accuracy in climate bias correction across multiple metrics.

Abstract

Climate models are essential for understanding large-scale climate dynamics and long-term climate change, yet they exhibit systematic biases when compared with historical observations. Existing multivariate bias correction (MBC) approaches do not explicitly handle spatiotemporal dependence. However, preserving both spatiotemporal and inter-variable consistency is essential for realistic climate dynamics and reliable regional impact assessments. To address this gap, we propose a novel MBC method called GN-VBC that uses generalized additive models (GAMs) to disentangle spatiotemporal deterministic effects from stochastic residuals. To model joint distributions and dependencies across variables and locations, we introduce nested vine copulas (NVCs), a hierarchical vine merging strategy. NVC in the context of MBC combines two dependence levels: (i) spatial dependence across locations, modeled separately for each variable, and (ii) inter-variable dependence modeled at a selected reference location, which links the spatial models into a coherent multivariate and spatial structure. An application to Switzerland shows improvements in preserving inter-variable, spatial and temporal dependence across a wide range of evaluation metrics.