EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Analysis of precipitation records from climate models in a non-stationary context

Paula Gonzalez1, Philippe Naveau1, Soulivanh Thao1, and Julien Worms2
Paula Gonzalez et al.
  • 1Laboratoire des sciences du climat et de l'environnement, ESTIMR, France
  • 2Laboratoire de Mathématiques de Versailles, Versailles

In the context of climate change, assessing how likely a particular change or event has been caused by human influence is important for mitigation and adaptation policies. Disturbances, such as increases in the frequency and intensity of extreme precipitation have been observed at continental to global scales. In this work we present an Extreme Event Attribution methodology for yearly maxima records that takes into consideration the temporal non-stationarity of climate variables and allow us to quantify record probability at a global scale in a transient setup. We apply our methodology to study records of yearly maxima of daily precipitation issued from the numerical climate model IPSL-CM6A-LR and the scenario rcp8.5 at a global scale. Focusing on decadal records, we detect a clear anthropogenic signal from the 2020's, even thought decadal record probability increases in most parts of the world, we observe a decrease of records probability in the subtropics.

How to cite: Gonzalez, P., Naveau, P., Thao, S., and Worms, J.: Analysis of precipitation records from climate models in a non-stationary context, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13786,, 2023.