EGU23-14253, updated on 26 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Downscaling with a machine learning-based emulator of a local-scale UK climate model

Henry Addison1, Elizabeth Kendon1,2, Suman Ravuri3, Laurence Aitchison1, and Peter Watson1
Henry Addison et al.
  • 1University of Bristol, Bristol, UK (
  • 2Met Office Hadley Centre, Exeter, UK
  • 3DeepMind, London, UK

High resolution rainfall projections are useful for planning for climate change [1] but are expensive to produce using physical simulations. We make novel use of a state-of-the-art generative machine learning (ML) method, diffusion models [2], to more cheaply generate high resolution (8.8km) daily mean rainfall samples over England and Wales conditioned on low resolution (60km) climate model variables. The downscaling model is trained on output from the Met Office UK convection-permitting model (CPM) [3]. We then apply it to predict high-resolution rainfall based on either coarsened CPM output or output from the Met Office HadGEM3 general circulation model (GCM). The downscaling model is stochastic and able to produce samples of high-resolution rainfall that have realistic spatial structure, which previous methods struggle to achieve. It is also easy to train and should better estimate the probability of extreme events compared to previous generative ML approaches.

The downscaling model samples match well the rainfall distribution of CPM simulation output. We use as our conditioning variables We obtained further improvements by also including high-resolution, location-specific parameters that are learnt during the ML training phase. We will discuss the challenges of applying the model trained on coarsened CPM variables to GCM variables and present results about the method’s ability to reproduce the spatial and temporal behaviour of rainfall and extreme events that are better represented in the CPM than the GCM due to the CPM’s ability to model atmospheric convection.


[1] Kendon, E. J. et al. (2021). Update to the UKCP Local (2.2km) projections. Science report, Met Office Hadley Centre, Exeter, UK. [Online]. Available:

[2] Song, Y. et al. (2021). Score-Based Generative Modeling through Stochastic Differential Equations. ICLR.

[3] Met Office Hadley Centre. (2019). UKCP18 Local Projections at 2.2km Resolution for 1980-2080, Centre for Environmental Data Analysis. [Online]. Available:

How to cite: Addison, H., Kendon, E., Ravuri, S., Aitchison, L., and Watson, P.: Downscaling with a machine learning-based emulator of a local-scale UK climate model, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14253,, 2023.

Supplementary materials

Supplementary material file