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

This study proposed a Comprehensive Index (CI) that jointly considers bias correction performance metrics and uncertainty to guide the selection of quantile mapping methods. This approach reveals not only a performance-based ranking of bias correction methods but also how optimal method choices shift as the uncertainty weight varies. This study evaluated daily precipitation performance from 11 CMIP6 GCMs corrected by Quantile Delta Mapping (QDM), Empirical Quantile Mapping (EQM), and Detrended Quantile Mapping (DQM) using ten evaluation metrics and applied TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) to compute performance-based rankings. Furthermore, Bayesian Model Averaging (BMA) was used to quantify both individual model and ensemble prediction uncertainties. Moreover, entropy based weighting of the ten evaluation metrics reveals that error based measures such as RMSE and MAE carry the highest information content (weights 0.13–0.28 and 0.15–0.22, respectively). By aggregating TOPSIS performance scores with BMA uncertainty measures, this study developed CI. Results show that EQM achieved the best performance across most metrics 0.30 (RMSE), 0.18 (MAE), 0.98 (R²), 0.87 (KGE), 0.93 (NSE), and 0.99 (EVS) and exhibited the lowest uncertainty (variance = 0.0027) across all continents. QDM outperformed other methods in certain regions, reaching its lowest model uncertainty (variance = 0.0025) in South America. EQM was selected most frequently under all weighting scenarios, while DQM was least chosen. In South America, DQM was preferred more often than QDM when performance was emphasized, whereas the opposite occurred when uncertainty was emphasized. These findings suggest that incorporating uncertainty leads to spatially heterogeneous and parameter dependent changes in optimal bias correction method choice that would be overlooked by metric only selection.