2027-B11 Learning the changing extremes of European precipitation in a warming climate
PROJECT HIGHLIGHTS
- Assessment of the climate change influence on extreme precipitation
- Method development for an AI-based diagnostic
- Improving our understanding how changes in extreme precipitation could work and unfold
Overview
One of the major challenges for the current and future climate diagnostics is the occurrence of very rare, but extreme intense precipitation events. Those record-shattering events lead to the Valencia-Flood in October 2024 in Spain or the Ahr-floods in July 2021 in Germany with billions of Euro of financial losses and unfortunately significant numbers of fatalities (cf. e.g., Leckebusch & McCarthy (2026) and references therein).
While the principal underlying physical mechanism of precipitation formation and the principal mechanisms of necessary precursor conditions are well understood, the exact amount of extreme precipitation and when and where those events may occur are less well understood. The same holds true for the principal rate of occurrence, which is difficult to estimate as those events may occur only once or twice per century in the current climate. Even more difficult is an assessment of those events for future climate conditions, altered by human activities. We can already diagnose that those events are more likely to occur under the influence of a warming climate, but the exact changes in those statistics of their occurrence remains a challenge. One of the reasons for this is the inability of current climate models to fully simulate those rare and extreme precipitation events in their full characteristics, from very local scale processes to the wider statistical occurrences for e.g., 30-years climatic time spans.
This project will develop and test a novel approach to estimate those rare events. It will investigate whether a combination of AI- and Extreme Value Theory (EVT)-approaches can produce reliable estimates for future climate states. Thus, can AI–EVT models trained on reanalyses and large hindcast ensembles estimate regional extreme-rainfall distributions that remain reliable when transferred between global warming levels and circulation regimes?
This project would best suit a student with a Computer Science background, or otherwise a numerical background for example Meteorology, Physics, or Maths, with experience coding in Python or similar (e.g. R, Matlab), and preferably with some experience in AI approaches.
Case Projects
This project does not offer a CASE studentship
Host
- University of Birmingham
Theme
- Climate and Environmental Sustainability
Supervisors
Project investigator
- GC Leckebusch, G.C.Leckebusch@bham.ac.uk
Co-investigators
- Russell Beale, r.beale@bham.ac.uk
- Leandro Minku, l.l.minku@bham.ac.uk
- Ruth Geen, r.geen@bham.ac.uk
How to apply
Each host has a slightly different application process.
Find out how to apply for this studentship.
All applications must include the CENTA application form.
Methodology
In the project the combined set-up between specific AI-modelling approaches and the implementation in the context of EVT to estimate tail events in the precipitation event distribution will be explored. The likely methodological work will include:
- Construct North Atlantic–European predictor fields and regional UK/European extreme-rainfall targets from reanalysis, UNSEEN-style hindcasts and CMIP-class simulations.
- Compare conventional statistical downscaling and GEV/GPD approaches with CNN, attention or graph-based tail emulators.
- Train across different pseudo warming-level environments—present climate through approximately +3°C.
- Test cross-warming-level transportability, calibration and extrapolation.
- Develop physics checks, uncertainty estimates and explicit “safe operating ranges”.
- Produce an open benchmark dataset, emulator and model passport.
Further details and How to Apply
For any enquiries related to this project please contact:
Lead supervisor:
Prof. Dr. Gregor C. Leckebusch, Chair of Meteorology and Climatology and UK Met Office Joint Chair
School of Geography, Earth and Environmental Sciences, University of Birmingham
W: http://www.birmingham.ac.uk/staff/profiles/gees/leckebusch-gregor.aspx
To apply to this project:
- You must include a CENTA studentship application form, downloadable from: CENTA Studentship Application Form 2027
- You must include a CV with the names of at least two referees (preferably three) who can comment on your academic abilities.
- Please submit your application and complete the host institution application process via: https://admissions.bham.ac.uk/course-finder-landing-page/?code=LES068 Please select the PhD in Geography (CENTA) 2027 entry year Apply Now button. The CENTA Studentship Application Form 2027 and CV can both be uploaded to the Personal Statement section of the online form. In the funding section of the online form please select Research Council Funding and then choose Natural Environment Research Council (NERC). Please quote CENTA 2027-B11 when completing the application form.
Applications must be submitted by 23:59 GMT on Wednesday 6th January 2027.
