CORDEX-ML-Bench benchmarks 40 ML models for climate downscaling and finds generative models outperform deterministic ones on precipitation while historically trained models underestimate future climate signals.
arXiv preprint arXiv:2512.13987 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
physics.ao-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Flow matching produces better spatial structure than diffusion models for convective precipitation downscaling but underestimates heavy rainfall amounts.
citing papers explorer
-
CORDEX-ML-Bench: A Benchmark for Data-Driven Regional Climate Downscaling -Experiment Design and Overview
CORDEX-ML-Bench benchmarks 40 ML models for climate downscaling and finds generative models outperform deterministic ones on precipitation while historically trained models underestimate future climate signals.
-
Flow Matching for Convective-Scale Precipitation Downscaling
Flow matching produces better spatial structure than diffusion models for convective precipitation downscaling but underestimates heavy rainfall amounts.