A new fast multivariate bias correction technique, EMBCCA-UNSEEN (v1.1.0): a case study of compound events in Hunan Province, China, using the UNSEEN approach

Abstract. The frequency of extreme climate events such as floods, droughts and heatwaves has been increasing due to anthropogenic climate change. However, the limited availability of historical data poses a significant challenge in accurately assessing these events and their likelihood. This study addresses this issue by employing the UNSEEN approach (UNprecedented Simulated Extremes using ENsembles) in combination with the UK Met Office's DePreSys4 initialized hindcast model to simulate a large ensemble of climate data. To enhance the reliability of the model data, we develop a multivariate bias and correlation correction method, Eigenvalue-based Multivariate Bias Correction with Correlation Alignment (EMBCCA-UNSEEN), which combines mean-shift correction and linear transformations to correct inter-variable correlations, ensuring consistency across multiple dimensions of the model data with the observational data. EMBCCA-UNSEEN v1.1.0 is distributed as an installable Python package (Yang et al., 2026). An evaluation of the corrected model data is conducted using two multivariate fidelity testing methods, including statistical feature assessment and a support vector machine-based testing approach. The test results demonstrate that applying the EMBCCA-UNSEEN correction method can significantly improve the consistency between the model data and the observational data, and is significantly more computationally efficient than alternative approaches.

Using a case study for Hunan Province, China, we demonstrate how the methodology improves the representation of extreme events and quantify the annual risk of extreme compound hot and dry events. The results indicate that the new bias correction method effectively captures inter-variable correlations while preserving variance, thereby enhancing the accuracy of simulations for compound extreme climate events.