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. A key challenge is determining how limited high-resolution simulations should be distributed across a changing climate trajectory to capture both forced climate response and internal variability. Using the CESM2 Large Ensemble over the western United States, we compare three training-year selection strategies under fixed data budget
The increasing computational demands of high-resolution climate modeling and the rapid advancement of machine learning techniques are converging to create opportunities for more efficient climate prediction.
Improving the efficiency and accuracy of climate downscaling through ML will provide better data for critical climate impact assessments, influencing policy and infrastructure decisions.
The methodology for designing training sets for ML climate models could become significantly more parsimonious, leading to faster and more resource-efficient climate simulations.
- · Climate scientists
- · Environmental policy makers
- · ML model developers
- · Governments
- · Traditional climate simulation methods
- · Regions unprepared for climate shifts
More accurate and localized climate change projections become available faster.
Improved projections lead to more effective adaptation and mitigation strategies, possibly influencing resource management.
Enhanced climate data could integrate into various sectors, from agriculture to urban planning, driving new market opportunities for climate-informed solutions.
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Read at arXiv cs.LG