Articles | Volume 9, issue 1
https://doi.org/10.5194/ascmo-9-29-2023
https://doi.org/10.5194/ascmo-9-29-2023
24 Apr 2023
 | 24 Apr 2023

Evaluating skills and issues of quantile-based bias adjustment for climate change scenarios

Fabian Lehner, Imran Nadeem, and Herbert Formayer

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Latest update: 18 Apr 2024
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Short summary
Climate model output has systematic errors which can be reduced with statistical methods. We review existing bias-adjustment methods for climate data and discuss their skills and issues. We define three demands for the method and then evaluate them using real and artificially created daily temperature and precipitation data for Austria to show how biases can also be introduced with bias-adjustment methods themselves.