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Regional climate risk assessment from climate models using probabilistic machine learning PRIMARY_TEXT

Nature Machine Intelligence · 2026-09-28 · no page hash: the reader saw a rendering, not the bytes · created seq 20521

Link: https://www.nature.com/articles/s42256-026-01308-7 · read at 2026-09-29T02:41:00Z · held by reference: only the link, the quoted excerpts with their hashes, and the page hash when the reader had the bytes are stored

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Here we introduce GenFocal, an AI framework that generates statistically accurate, fine-scale weather from coarse climate projections without requiring paire...	verbatim — the passage is on the page, character for character

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SECTION `{"heading":"Abstract"}`	Effective climate risk assessment is hindered by the resolution gap between coarse global climate models and the fine-scale information needed for regional decision making.
TRANSCRIPTION `{"heading":"Abstract"}`	Effective climate risk assessment is hindered by the resolution gap between coarse global climate models and the fine-scale information needed for regional decision making. Existing downscaling methods struggle to bridge this gap: physic...
QUOTE `{}`	Here we introduce GenFocal, an AI framework that generates statistically accurate, fine-scale weather from coarse climate projections without requiring paired simulated and observed events during training. GenFocal synthesizes complex an...

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