Intercomparison of bias correction methods for precipitation of multiple GCMs across six continents

Abstract. This study, conducted across six continents, evaluated and compared the effectiveness of three Quantile Mapping (QM) methods: Quantile Delta Mapping (QDM), Empirical Quantile Mapping (EQM), and Detrended Quantile Mapping (DQM) for correcting daily precipitation data from 11 CMIP6 General Circulation Models (GCMs). The performance of corrected precipitation data was evaluated using ten evaluation metrics, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was applied to calculate performance-based priorities. Bayesian Model Averaging (BMA) was used to quantify model-specific and ensemble prediction uncertainties. Subsequently, this study developed a comprehensive index by aggregating the performance scores from TOPSIS with the uncertainty metrics from BMA. The results showed that EQM performed the best on all continents, effectively managing performance and uncertainty. QDM outperformed other methods in specific regions and was selected more frequently than DQM when greater weight was given to uncertainty. It suggests that daily precipitation corrected by QDM is more stable than DQM. On the other hand, DQM effectively reproduces dry climate but shows the highest uncertainty in certain regions, suggesting potential limitations in capturing long-term climate trends. This study emphasizes that both performance and uncertainty should be considered when choosing a bias correction method to increase the reliability of climate predictions.