Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 11
Articles | Volume 17, issue 11
https://doi.org/10.5194/gmd-17-4689-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-4689-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 11
Methods for assessment of models
 | 
13 Jun 2024
Methods for assessment of models |  | 13 Jun 2024

Multivariate adjustment of drizzle bias using machine learning in European climate projections

Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld

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Short summary
This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
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