Evaluating the Performance of Uni- and Multivariate Bias Correction Techniques: Challenges in Preserving Temporal and Dependence Structures

Global Climate Models (GCMs) are essential for simulating past and future climates but suffer from systematic biases and coarse resolution, limiting direct applications. Bias correction (BC) and downscaling, using dynamical or statistical methods, address these issues. Quantile mapping (QM)-based BC is widely used, yet it distorts dependencies, prompting multivariate approaches whose assumptions remain unclear and results inconsistent. This study evaluates four BC techniques, including one univariate (QM) and three multivariate (dOTC, R2D2, MBCn), in correcting univariate, multivariate, and temporal features of daily precipitation and temperature over India during Indian Summer Monsoon (ISM). For univariate metrics, dOTC effectively corrected temperature mean, variance, and extremes, while QM and dOTC best addressed precipitation variance. Further, R2D2 was most effective for mean correction, and MBCn for dry days and P90. Among multivariate methods, R2D2 best preserved inter-variable dependencies, whereas MBCn better captured temporal features, especially precipitation autocorrelation. Additionally, the study evaluates the effectiveness of BC techniques to preserve intervariable dependence, focusing on the Pacific Walker circulation constructed using causal network, crucial for capturing complex climate signals. None of the techniques, however, reproduced the observed network across all GCMs. The overall performance of BC methods was evaluated by averaging ranks across categories since no single approach consistently excelled across all metrics. Among the techniques, dOTC showed the best overall performance, while R2D2 achieved the highest ranks in multivariate evaluations. The findings offer practical insights and highlight challenges in selecting appropriate BC methods for climate applications.