Advancing multivariate bias correction with copulas: a robust approach for preserving rank correlation in climate model accuracy

Global Climate Models (GCMs) are indispensable for future climate projections, yet they often contain inherent inaccuracies. Traditional bias correction methods, primarily one-dimensional, fall short in accurately addressing the complexity inherent in climate data. While two-dimensional methods, such as the approaches proposed by researchers such as Piani, represent a significant advancement, they too have their limitations, particularly in maintaining the rank correlation between variables. This study introduces a refined two-dimensional bias correction method that specifically addresses the shortcomings in Piani's approach, particularly focusing on preserving the rank correlation between maximum temperature and precipitation. This enhanced methodology was applied to a dataset encompassing 60 weather stations across Korea, covering a broad spectrum of climatic conditions for 10 different GCMs The results show a marked improvement over Piani's method, notably in the accuracy of modeling inter-variable relationships. This refinement offers a more robust tool for climate data analysis and projection, especially pertinent in hydrological and climatological studies where the precise understanding of temperature-precipitation dynamics is vital.