Articles | Volume 14, issue 1
Research article
 | Highlight paper
26 Jan 2023
Research article | Highlight paper |  | 26 Jan 2023

Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change

Iris Elisabeth de Vries, Sebastian Sippel, Angeline Greene Pendergrass, and Reto Knutti


Total article views: 3,420 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,867 502 51 3,420 124 38 42
  • HTML: 2,867
  • PDF: 502
  • XML: 51
  • Total: 3,420
  • Supplement: 124
  • BibTeX: 38
  • EndNote: 42
Views and downloads (calculated since 13 Jul 2022)
Cumulative views and downloads (calculated since 13 Jul 2022)

Viewed (geographical distribution)

Total article views: 3,420 (including HTML, PDF, and XML) Thereof 3,335 with geography defined and 85 with unknown origin.
Country # Views %
  • 1


Latest update: 20 Jun 2024
Chief editor
Detecting and attributing forced precipitation changes is a long-standing challenge in climate science. This study proposes an approach to efficiently extract information on forced precipitation changes from climate data and models, which can be valuable both from a scientific and policy-making perspective.
Short summary
Precipitation change is an important consequence of climate change, but it is hard to detect and quantify. Our intuitive method yields robust and interpretable detection of forced precipitation change in three observational datasets for global mean and extreme precipitation, but the different observational datasets show different magnitudes of forced change. Assessment and reduction of uncertainties surrounding forced precipitation change are important for future projections and adaptation.
Final-revised paper