Machine learning emulation of precipitation from km-scale regional climate simulations using a diffusion model (2407.14158v2)

Abstract: High-resolution climate simulations are valuable for understanding climate change impacts. This has motivated use of regional convection-permitting climate models (CPMs), but these are very computationally expensive. We present a convection-permitting model generative emulator (CPMGEM), to skilfully emulate precipitation simulations by a 2.2km-resolution regional CPM at much lower cost. This utilises a generative machine learning approach, a diffusion model. It takes inputs at the 60km resolution of the driving global climate model and downscales these to 8.8km, with daily-mean time resolution, capturing the effect of convective processes represented in the CPM at these scales. The emulator is trained on simulations over England and Wales from the United Kingdom Climate Projections Local product, covering years between 1980 and 2080 following a high emissions scenario. The output precipitation has a similarly realistic spatial structure and intensity distribution to the CPM simulations. The emulator is stochastic, which improves the realism of samples. We show evidence that the emulator has skill for extreme events with ~100 year return times. It captures the main features of the simulated 21st century climate change, but exhibits some error in the magnitude. We demonstrate successful transfer from a "perfect model" training setting to application using GCM variable inputs. We also show that the method can be useful in situations with limited amounts of high-resolution data. Potential applications include producing high-resolution precipitation predictions for large-ensemble climate simulations and producing output based on different GCMs and climate change scenarios to better sample uncertainty.