Attribution of convective rainfall events using AI-downscaling – how extreme can we go?

Probabilistic attribution of extreme events requires large-ensemble climate model simulations, for both present and counterfactual climates, to adequately capture the tails of the distribution. Accurately modelling rainfall extremes, particularly those involving convection, or rainfall over regions with complex topography, requires high-resolution climate models. High-resolution climate data is particularly important for impact attribution to simulate realistic flood inundation as input to flood models.

Large ensembles of climate model runs for pre-industrial climates do not currently exist at convection-permitting resolution, as conventional convection-permitting models are computationally expensive to run. Therefore, attribution studies on extreme localised convective rainfall events are limited, despite the large impacts these events have on society.

To address this, we create a convective-permitting-resolution, large-ensemble dataset for England and Wales using a generative AI approach to downscale a pre-existing large ensemble of attribution runs from the HadGEM3 climate model. We use the diffusion model CPMGEM from Addison et al. (2025), which is trained and tested on the convection-permitting-resolution UK local Climate Projections data. We use CPMGEM, which enables stochastic generation of multiple samples per coarse model input, to generate multiple high-resolution precipitation samples from our original large-ensemble dataset. This process is relatively computationally cheap and enables creation of a high-resolution dataset that is larger than the input dataset.

We first investigate the ability of CPMGEM to be applied to a different configuration of the model it was trained on, and on an alternative set of counterfactuals. We also explore its ability to conserve climate trends and reproduce realistic values for the extremes.

We then assess the validity of using the downscaled dataset for attribution studies. If suitable, we will revisit a number of relevant attribution studies of extreme rainfall events and compare the original results from the coarse climate model HadGEM3-A to our new results using the high-resolution downscaled CPMGEM output. Overall, this could significantly extend the capability to attribute localised extreme rainfall events.