Articles | Volume 16, issue 2
https://doi.org/10.5194/esd-16-607-2025
https://doi.org/10.5194/esd-16-607-2025
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

Viewed

Total article views: 382 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
257 64 61 382 30 23 21
  • HTML: 257
  • PDF: 64
  • XML: 61
  • Total: 382
  • Supplement: 30
  • BibTeX: 23
  • EndNote: 21
Views and downloads (calculated since 06 Sep 2024)
Cumulative views and downloads (calculated since 06 Sep 2024)

Viewed (geographical distribution)

Total article views: 382 (including HTML, PDF, and XML) Thereof 382 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 Apr 2025
Download
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.
Share
Altmetrics
Final-revised paper
Preprint