PhD Studentship: CENTA 2027-B11 Learning the changing extremes of European precipitation in a warming climate

University of Birmingham; Birmingham; On-site; GBP 18,000 - 20,000; Full time

Benefits offered by this job

Stipend funded by grant

Tuition fees covered

Research Training Support Grant

Job summary

The University of Birmingham invites applications for a PhD project that combines AI and Extreme Value Theory to estimate rare precipitation events under future climate conditions. You will work with reanalysis data and hindcast ensembles using Python and related tools.

The project, funded by NERC CENTA3 DLA, covers stipend, tuition fees at home rate, and a Research Training Support Grant; suitable for students with strong computational backgrounds in Computer Science, Meteorology, Physics, or

Qualifications

• 2:1 at UK BSc level or MSc level or equivalent.
• Experience coding in Python or similar (e.g. R, Matlab).
• International students eligible for funding up to 30% of cohort; English language requirements apply.

Responsibilities

• Develop AI- and EVT-based methods for estimating rare extreme rainfall events.
• Test approaches on reanalyses and hindcast ensembles.
• Work with climate data to quantify regional extreme-rainfall distributions under different warming levels.

Skills

Python

AI

EVT theory

Education

UK 2:1 / MSc equivalent

Tools

R

MATLAB

Job description

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- and 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.

Funding notes:

This project is offered through the CENTA3 DLA, funded by the Natural Environment Research Council (NERC). Funding covers: annual stipend, tuition fees (at home-fee level), Research Training Support Grant.

Academic requirements: at least a 2:1 at UK BSc level or a pass at UK MSc level or equivalent.

International students are eligible for studentships to a maximum of 30% of the cohort. Funding does not cover any additional costs relating to moving or residing in the UK. International applicants must fulfil the University of Birmingham's international student entry requirements, including English language.

References:

Angus, M., and G.C. Leckebusch, 2020: On the dependency of Atlantic Hurricane and European Windstorm Hazard. Geophysical Research Letters, 47(23), https://doi.org/10.1029/2020GL090446

Angus, M., M. Widmann, A. Orr, R. Ashrit, G.C. Leckebusch, and A. Mitra, 2024: A comparison of two statistical postprocessing methods for heavy precipitation forecasts over India during the summer monsoon. QJR Meteorol. Soc. 2024; 1–19, http://doi.org/10.1002/qj.4677

Beale, R. et al, 2025: AI4Climate Policy brief: AI will not replace climate science - but it can make it fit for an age of extremes. https://www.birmingham.ac.uk/news/2025/ai-will-not-replace-climate-science-but-it-can-make-it-fit-for-an-age-of-extremes