Beyond Pointwise Error: A Multi-Metric Evaluation of Spatial Climate Downscaling
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arXiv:2607. 03279v1 Announce Type: new Abstract: Accurate regional weather prediction requires resolving fine-scale structure while remaining consistent with global dynamics.
arXiv:2604. 03275v2 Announce Type: replace-cross Abstract: Effective adaptation and mitigation strategies for climate change require high-resolution projections to inform strategic decision-making.
arXiv:2609.39833v1 Announce Type: new Abstract: Extreme rainfall events are increasing in intensity and frequency as climate change accelerates. While kilometer-scale precipitation forecasts are crit...
The paper introduces lightweight probabilistic downscaling models that build on a modified U‑Net backbone, adapting two recent machine learning techniques from weather forecasting. Using a two‑stage training curriculum—deterministic pretraining followed by probabilistic fine‑tuning—the authors evaluate their models on the CORDEX‑ML‑Bench suite for daily maximum temperature and precipitation in the Alps, New Zealand, and South Africa. The results show that this approach outperforms the current state‑of‑the‑art in RMSE, offering a more computationally efficient method for generating fine‑resolution regional climate data.
arXiv:2609.40140v2 Announce Type: cross Abstract: Extreme heat is where urban adaptation needs kilometer-scale data the most, but the simulations training a downscaler can cost more than they save, a...
arXiv:2607. 05292v1 Announce Type: cross Abstract: Super-resolving coarse atmospheric fields to local PM$_{2.