RIME-X v1.0: combining simple climate models, Earth system models, and climate impact models into a unified statistical emulator for regional climate indicators

Schwind, N., Perrette, M., Byers, E. ORCID: https://orcid.org/0000-0003-0349-5742, Högner, A. ORCID: https://orcid.org/0000-0002-4178-9664, Lejeune, Q., Möller, T., Nicholls, Z. ORCID: https://orcid.org/0000-0002-4767-2723, Pfleiderer, P., Schongart, S., Werning, M. ORCID: https://orcid.org/0000-0002-8684-9608, & Schleussner, C.-F. ORCID: https://orcid.org/0000-0001-8471-848X (2026). RIME-X v1.0: combining simple climate models, Earth system models, and climate impact models into a unified statistical emulator for regional climate indicators. Geoscientific Model Development 19 (14) 6797-6828. 10.5194/gmd-19-6797-2026.

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Abstract

Many tasks in climate science, including climate impact assessment, scenario analysis, and end-to-end attribution, require efficient methods to translate a wide range of emissions scenarios into regional-scale climate indicators while explicitly accounting for uncertainty. Climate and impact model emulators are statistical models that approximate selected outputs of comprehensive models and can perform this translation. The Rapid Impact Model Emulator (RIME) uses individual simulations from climate or impact models to empirically relate global mean surface air temperature (GMT) levels to regional-scale indicators, enabling the conversion of GMT trajectories, commonly derived from Simple Climate Models (SCMs), into time series of regional climate impacts. Here, we present the Rapid Impact Model Emulator Extended (RIME-X), an extension of the RIME framework that replaces deterministic emulation of individual models along single GMT trajectories with a probabilistic approach. RIME-X combines ensemble simulations of GMT derived from SCMs with warming-level-dependent regional indicator distributions estimated from weighted Model Intercomparison Project (MIP) data. This results in scenario-dependent, time-evolving probability distributions of those regional indicators. By jointly quantifying global and regional sources of uncertainty from the start, RIME-X enables systematic exploration of the full space of plausible regional climate impact trajectories under different emissions scenarios. The method is conceptually applicable to regional indicators whose distributions are predominantly determined by warming level and provides a computationally efficient framework for uncertainty-aware regional indicator emulation. We evaluate the method using out-of-sample validation on temperature and precipitation simulations from the Coupled Model Intercomparison Project 6 (CMIP-6) and demonstrate its applicability to a range of impact and extreme event indicators derived from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). We provide an open-source Python implementation of RIME-X, including preprocessing workflows for data from ISIMIP and support for user-defined indicators.

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