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Bias correction is usually applied to climate model outputs before they are used as inputs to environmental models for impact studies. Every climate model is post-processed independently of others to account for biases originating from model structure and internal variability. To better understand the role of internal variability, multi-member ensembles (multiple runs of a single climate model, with identical forcing but different initial conditions) have now become common in the modeling community. Bias correcting such ensembles requires specific considerations. Correcting all members of such an ensemble independently would force all of them to the target distribution, thus removing the signature of natural variability over the calibration period. How this undesirable effect would propagate onto subsequent time periods is unknown. This study proposes three bias correction variants of a multi-member …