A generative likelihood framework for high-resolution climate model evaluation

Journal article

Abstract Next-generation high-resolution (km-scale) climate models promise unprecedented accuracy in climate projections, but realizing their potential requires robust methods to quantify how well simulations align with real-world observations. Average-based metrics conventionally used for climate model evaluation ignore the physics encoded in the fine-scale structures of km-scale simulations. To overcome this limitation, we propose a novel, statistically principled evaluation methodology based on the likelihood function of a generative image model. Our method provides a continuous similarity metric derived from the likelihood distribution of observation and simulation snapshots, which can redefine the evaluation, intercomparison, and parameter tuning of high-resolution climate models. We demonstrate the applicability and interpretability of this method by evaluating convective clouds simulated by two state-of-the-art global km-scale models, using their outgoing infrared radiation fields. This work establishes a scalable pathway toward observation-based evaluation of next-generation climate simulations.

Authors: Freischem, LJ; Reichelt, T; Clark, R; Stier, P; Christensen, H

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Cambridge University Press
• Journal: Environmental Data Science
• Volume: 5
• Publication date: 2026-07-14
• DOI: 10.1017/eds.2026.10054
• EISSN: 2634-4602
• ISSN: 2634-4602
• Language: English
• Keywords: Function (biology); Interpretability; Generative model; Similarity (geometry); Climate model; Metric (unit); Scalability