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.
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arXiv:2602. 13416v2 Announce Type: replace Abstract: The proliferation of data-driven models in weather and climate sciences has marked a significant paradigm shift, with advanced models demonstrating exceptional skill in medium-range forecasting.
arXiv:2606. 14570v1 Announce Type: cross Abstract: Emulators provide a cost-effective alternative to regional climate models (RCMs) by capturing their dynamical downscaling function.
arXiv:2607. 03279v1 Announce Type: new Abstract: Accurate regional weather prediction requires resolving fine-scale structure while remaining consistent with global dynamics.
arXiv:2607. 17037v1 Announce Type: new Abstract: High-resolution atmospheric data are required to resolve mesoscale and localized meteorological structures, however such datasets remain limited in many regions of the world.
arXiv:2606. 07898v1 Announce Type: new Abstract: High-resolution regional climate simulations provide critical information for climate impacts assessments but remain computationally expensive, motivating the development of machine-learning downscalers and emulators.
arXiv:2607. 05645v1 Announce Type: new Abstract: Deep-learning-based climate downscaling aims to learn relationships from historical low-resolution (LR) and high-resolution (HR) climate data to generate HR climate projections.