Chemistry–climate models show biases compared to surface ozone observations and thus require bias correction for impact studies and the assessment of air quality changes. We compare the performance of commonly used correction techniques for model outputs available via CMIP6. While all methods can reduce model biases, better results are obtained from more complex approaches. Thus, our study suggests broader use of these techniques in studies seeking to inform air quality management and policy.