Constraining uncertainty in projected precipitation over land with causal discovery

Debeire, Kevin; Bock, Lisa; Nowack, Peer; Runge, Jakob; Eyring, Veronika

doi:https://doi.org/10.5194/esd-16-607-2025

Articles | Volume 16, issue 2

https://doi.org/10.5194/esd-16-607-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/esd-16-607-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 2

Research article

|

24 Apr 2025

Research article |

| 24 Apr 2025

Constraining uncertainty in projected precipitation over land with causal discovery

Kevin Debeire, Lisa Bock, Peer Nowack, Jakob Runge, and Veronika Eyring

Supplement

https://doi.org/10.5194/esd-16-607-2025-supplement

Data sets

Coupled Model Intercomparison Project Phase 6 (CMIP6) data CMIP https://esgf-metagrid.cloud.dkrz.de/search

ERA5 hourly data on single levels from 1940 to present Copernicus Climate Change Service, Climate Data Store https://doi.org/10.24381/cds.adbb2d47

NCEP-NCAR reanalysis 1 NOAA/NCEP https://www.psl.noaa.gov/data/gridded/data.ncep.reanalysis.html

jakobrunge/tigramite: Tigramite 5.2 J. Runge https://doi.org/10.5281/ZENODO.7747255

Model code and software

Constraining uncertainty in projected precipitation over land with causal discovery K. Debeire https://doi.org/10.5281/zenodo.14865765

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Short summary

Projecting future precipitation is essential for preparing for climate change, but current climate models still have large uncertainties, especially over land. This study presents a new method to improve precipitation projections by identifying which models best capture key climate patterns. By giving more weight to models that better represent these patterns, our approach leads to more reliable future precipitation projections over land.