Effects of climate change on extreme weather events

Using 'distributional downscaling' to predict the effects of climate change on extreme weather events


State-of-the-art climate models typically run with horizontal resolutions of 50-100km, which is not sufficient to represent the impact of clouds or small-scale topography on local precipitation, thus making it impossible to predict local details of heavy precipitation events such as the 2013-14 UK winter floods in England and Wales. As the effects of climate change become more evident, it will be crucial to predict and assess the impact of extreme weather events with return times of 100 years or more, with regional accuracy to give civil authorities the best opportunity to prepare emergency response plans. 

Explaining the science

The weather/climate variables (eg. temperature, air velocity, air pressure etc.) can be seen as a vector valued variable W(x, t), evolving over three-dimensional space x and time t. Due to the significant cost of global climate model (GCM) simulations, climate predictions by GCMs are done using a coarse-resolution grid. Given these coarse-grained predictions of W only occur at n-many grid points of the coarse-grid on Earth’s surface by GCMs at a time-point, to assess the effects at a regional level the values of W need to be predicted at a finer spatial resolution based on some vector-valued predictor function p(x) (eg. topology, tree cover, water body etc.).

As these extreme climate events are the events from the tail of the distributions of W, this project proposes to predict the distribution of W for all values of x using techniques from distribution regression, given number of samples from the distributions of W at each of the nth points of the coarse-grid. This is called distributional downscaling as it involves predicting probability distributions rather than some statistics of the distribution.

Project aims

The outputs and impact will be both academic (through publications and conference presentations) and applied (prediction of extreme flooding).


  • Adaptation of the algorithms by European Centre for Medium-Range Weather Forecasts (ECMWF): Dr Dueben will evaluate whether the developed products could be used for post-processing of operational weather forecasts at ECMWF, in particular for ensemble and seasonal predictions. Considering ECMWF is the nodal meteorological research institute of Europe, the impact here could be very high.
  • Prediction of extreme flooding: Dr Watson is working with the Centre for Ecology and Hydrology and global insurers MS Amlin and SCOR, who could apply the results to better quantify risks from extreme flooding.