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Abstract: Machine Learning for Efficient Climate Downscaling: Balancing Data Parsimony, Accounting for Time-Stationarity, and Ensuring Accessibility (AGU25)

Machine Learning for Efficient Climate Downscaling: Balancing Data Parsimony, Accounting for Time-Stationarity, and Ensuring Accessibility

Karandeep Singh1, Stefan Rahimi2, Lei Huang1, Chad William Thackeray3, Benjamin Bass4, Jesse Norris3 and Alexander D Hall5, (1)University of California Los Angeles, Los Angeles, United States, (2)University of Wyoming, Atmospheric Science, Laramie, United States, (3)University of California Los Angeles, Atmospheric and Oceanic Sciences, Los Angeles, United States, (4)University of California Los Angeles, Atmoshperic and Oceanic Sciences, Los Angeles, CA, United States, (5)University of California Los Angeles, Atmospheric and Oceanic Sciences, Los Angeles, CA, United States
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