Q-SRDRN multi-quantile network with pinball loss and per-quantile heads detects extreme precipitation events up to 18 times more effectively than deterministic baselines while preserving augmentation benefits for the median.
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2026 2verdicts
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SerpentFlow aligns large-scale wind patterns across GCM and observational domains then uses flow-matching to generate consistent fine-scale multivariate wind fields, outperforming standard bias correction in spatial coherence and robustness.
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Multi-Quantile Regression for Extreme Precipitation Downscaling
Q-SRDRN multi-quantile network with pinball loss and per-quantile heads detects extreme precipitation events up to 18 times more effectively than deterministic baselines while preserving augmentation benefits for the median.
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Generative Unsupervised Downscaling of Climate Models via Domain Alignment: Application to Wind Fields
SerpentFlow aligns large-scale wind patterns across GCM and observational domains then uses flow-matching to generate consistent fine-scale multivariate wind fields, outperforming standard bias correction in spatial coherence and robustness.