climate modeling 1computational efficiency 1interdisciplinary methods 1machine learning emulators 1model reliability 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.LG2026
How Can Machine Learning Emulators Best Support Climate Science?
Luca Schmidt, Nina Effenberger, Vitus Benson +5
The paper examines how machine‑learning emulators can be designed and deployed to reduce the computational cost of physics‑based climate models, proposing a framework that emphasiz…
cs.LG2025
A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation
Jonathan Schmidt, Luca Schmidt, Felix Strnad +2
Local climate information is crucial for impact assessment and decision-making, yet coarse global climate simulations cannot capture small-scale phenomena. Current statistical down…
cs.CV2025
RainShift: A Benchmark for Precipitation Downscaling Across Geographies
Paula Harder, Luca Schmidt, Francis Pelletier +5
Earth System Models (ESM) are our main tool for projecting the impacts of climate change. However, running these models at sufficient resolution for local-scale risk-assessments is…