  High-resolution climate simulations are very valuable for understanding
climate change impacts and planning adaptation measures. This has motivated use
of regional climate models at sufficiently fine resolution to capture important
small-scale atmospheric processes, such as convective storms. However, these
regional models have very high computational costs, limiting their
applicability. We present CPMGEM, a novel application of a generative machine
learning model, a diffusion model, to skilfully emulate precipitation
simulations from such a high-resolution model over England and Wales at much
lower cost. This emulator enables stochastic generation of high-resolution
(8.8km), daily-mean precipitation samples conditioned on coarse-resolution
(60km) weather states from a global climate model. The output is fine enough
for use in applications such as flood inundation modelling. The emulator
produces precipitation predictions with realistic intensities and spatial
structures and captures most of the 21st century climate change signal. We show
evidence that the emulator has skill for extreme events up to and including
1-in-100 year intensities. Potential applications include producing
high-resolution precipitation predictions for large-ensemble climate
simulations and downscaling different climate models and climate change
scenarios to better sample uncertainty in climate changes at local-scale.
