Assessing spatially explicit sensitivities to scenario uncertainty through climate emulation
Comprehensive climate risk assessment requires bridging the gap between the socio-economic detail of integrated assessment models and the physical fidelity of Earth System Models (ESMs). However, the high computational cost of ESMs limit their ability to explore the full range of uncertainty across myriad potential emissions scenarios. Here, we present an impact assessment framework coupling the MIT Emissions Projection and Policy Analysis (EPPA) model with the MIT Earth Sampler, a generative diffusion-based climate emulator. The emulator rapidly generates realizations of spatially- and cross-correlated climate fields at a fraction of the computational cost of an ESM. Benchmarking against a pattern scaling technique utilized by the MIT Integrated Global Systems Model (IGSM) confirms Earth Sampler's ability to reproduce several impact-relevant climate variables. We utilize this generative emulation framework to assess local and regional sensitivities to various emissions scenarios, including an early assessment of the projected ScenarioMIP-CMIP7 protocol. Results show that internal variability masks regional climate outcomes between globally distinct scenarios (e.g., 2°C vs. 1.5°C). Furthermore, we demonstrate that temperature overshoot pathways result in substantially higher cumulative heat stress risks compared to stabilization pathways with similar end-of-century outcomes. Generative climate emulation democratizes access to detailed climate projections, enabling the rapid, probabilistic assessment of compound climate hazards essential for robust adaptation and mitigation planning.