Systematic comparison of emulation techniques for regional climate under temperature overshoot scenarios

How regional climate change evolves in overshoot scenarios, in particular after the global mean temperature (GMT) peak, is not well understood. To investigate regional changes under overshoot, we develop an emulator that predicts trends in regional climate variables at the spatial level of IPCC regions from GMT time series, with applicability both before and after overshoot.

A commonly used approach to relate regional climate change to GMT is pattern scaling, which assumes a linear relationship between GMT and regional climate variables. Previous studies indicate limitations in applying pattern scaling under post-overshoot conditions, a finding that is also reflected in results produced as part of our emulator development.

We therefore apply a range of alternative techniques to solve the regional climate trend emulation problem. These include approaches based on the existing literature, such as impulse response functions and operator approximation, as well as machine-learning-based methods, including Gaussian process regression, random forests, XGBoost, state space models, and pre-trained deep-learning-based time series prediction techniques. All methods are trained on overshoot and non-overshoot simulations from CMIP6, Flat10MIP, and additional model experiments available in the literature.

We assess the performance of each approach under overshoot scenarios and compare them with simple pattern scaling used as a baseline to assess approach performance. We introduce an evaluation framework for emulations under long-term stabilisation and overshoot pathways that accounts for whether regional climate signals are reversible or irreversible and enables robust detection of overshoot and stabilisation dynamics.