| 09 Feb 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
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 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 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 provide an open-source Python implementation of RIME-X, including preprocessing workflows for data from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) and support for user-defined indicators.
This paper describes the RIME-X framework, a novel approach to simulating distributional climate impacts as a function of emissions pathways, exploiting probabilistic simple climate model ensembles and a conditional methodology which expresses regional impacts as a function of global mean temperture, using existing databases of climate simulations to build a lookup table which can be weighted according to model performance and interdepedence. The result is a model framework which can be used to produce quasi-probabilistic impact projections for novel scenarios with minimal computational effort.
The method is a smart approach to climate impact emulation, exploiting available data to make a reasonable approximation of probabilistic outcomes at the point level. The approach is pragamatic and natively probabilistic, the results are meaningful and easily interpretable. As such, the model is enormously useful as a way of utilizing ESM ensemble output while addressing ensemble biases and allowing exploration of a wider scenario space. The method also naturally pairs with existing work on model interdependency (further development on this front would be valuable, and I'd encourage the authors to do so).
In all - this is a comprehensive model description paper for a promising and elegant technique. I suggest some minor issues with the current submission which could be addressed in order to better frame the model's limitations for end users:
1. The key limitation of the approach (an assumed mapping between global mean temperature and impacts) - is acknowledged by the authors as limiting applibability to questions of path depedence, such as deep overshoot where warming patterns may significantly differ to a non-mitigation scenario. The extended work on overshoot impacts by many of the coauthors exemplifies this concern. Though this limitation is acknowledged - a useful extension would be to apply the technique to a scenario like SSP534over and gauge which variables are impacted by path dependency, and to what degree. Similarly, it would be useful to operationally test with SSP370 as a test of applicability to strongly differing aerosol emissions pathways. The authors could also consider what the optimal training set should be - e.g. is it an asset to keep SSP585 in the training set, if that scenario exhibits high warming rates which are unlike policy-relevant scenarios and it might actually reduce performance?
2. Out of sample validation of tempature/precip emulations are convincing and well presented. But while the model is demonstrated for its use in impact emulation (extremes, crop yields etc), and these results are highlighted in the abstract, validation is missing for these quantities. Some assessment of the out-of-sample performance of impact-relevant metrics would be very useful to demonstrate the model is viable for real-world applications. This is especially true given the model use in the Climate Impact Explorer - where end-of-chain results are the focus, but they are not properly validated. For these end-user applications, an assessment of reliability as a function of impact would be invaluable. For the purposes of a development-model description paper, this is all understandable - but the title/abstract should be slightly adjusted to create a clearer impression of what is validated and what is not.
3. Code is generally well written - seperation of preprocessing and emulation, well structured config files and dependencies. However - the lack of unit testing is a significant concern for robustness of future development - and I would highly recommend the authors to implement a formal test suite. More documentation & tutorials would also improve the usability of the codebase for new users.
The code also seems to slighly differ from the paper in the mapping of GMT distributions to impact distributions. The paper describes a Monte Carlo sampling of temperatures from the SCM ensemble distribution, for each sample drawing corresponding impact values from the conditional distribution. The code instead converts the GMT ensemble into quantiles, then uses linear interpolation between pre-computed GMT bins to obtain impact values. This is a sensible approach, but slightly conceptually different from the paper's description and relies on the validity of linear interpolation between GMT bins. This should be discussed.
4. Compound events - RIME-X is demonstrated to produce marginal distributions for temperature and precip conditional on global temperature. A useful extension would be to think about joint conditional distributions of multiple variables - which cannot be trivially inferred from the single distributions. This should be discussed as a limitation (as in a caveat not to trust the implicit joint distributions from the current model), and a possible future extension could be to do this formally.
5. The 21-year running mean is deeply embedded as part of the pre-processing step, but this step smooths over annual variability and infrequent features, and potentially cutting off an important part of the impact space associated with even mildly extreme years. The implication of this should be discussed more - how can the framework inform about once per decade events which might in fact be the dominant impacts on infrastructure - and this could be an avenue the authors explore in future versions.
6. It would be useful to provide guidance for end-users on where the model is currently reliable, where it's merely informative and where it's just hypothetical. The provided validation indicates that the model is relatively robust at coarse-scale: temperature/precip projections under non-overshoot scenarios. Downstream Impacts, and high-frequency outputs are perhaps less robust - and it would be useful for end-users to flag these as areas in development, as well as scenarios where the underlying assumptions would be expected to fail (large aerosol signals, SRM, large overshoots, impacts dominated by land use assumptions). Laying these out (especially in the impact explorer) would strengthen the model framework in terms of providing areas of operational applicability.