Dynamical-Model-Informed Stochastic Flow Matching for Global Probabilistic Subseasonal Forecasting of Means and Extremes
Abstract
Physics-based models provide skillful subseasonal forecasts but suffer from systematic biases, weakened teleconnections, and parameterized physics, while very large ensembles remain computationally expensive. Purely data-driven models also see too few realizations of the low-frequency modes that provide subseasonal predictability. We introduce methodname, a hybrid framework that converts modelshort subseasonal ensemble guidance into 90-member stochastic forecasts of weekly precipitation and 2 m temperature on a global 1-degree grid using a lead-aware flow-matching U-Net. Its stochastic prior initializes members with EOF perturbations conditioned on the Madden-Julian Oscillation, North Atlantic Oscillation, El Nino-Southern Oscillation, and lead week, allowing the learned flow to refine large-scale covariance rather than create it from white noise. On held-out 2021-2025 forecasts, empirical CRPS improves by 22.0% for precipitation and 25.6% for temperature in a same-initialization four-versus-four comparison, and by 32.2% and 34.0% in the 90-versus-four production comparison. Ablations across five training seeds show distinct gains from the structured prior and a learned error-variance pathway, with the prior contributing more. Verification over 120 observed-extreme cases shows positive CRPS skill, but the ensemble remains underdispersive, four-member upper quantiles are poorly resolved, and precipitation exceedance BSS is near neutral after week 1. Results support improved probabilistic skill and tail representation, but not calibrated operational rare-event probabilities. Case studies of the 2022 UK heatwave, 2023 California atmospheric rivers, and 2022 Pakistan floods illustrate the reach and limits of dynamically informed refinement.
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BibTeX
@misc{fahad2026dynamicalmodelinformed,
title = {{Dynamical-Model-Informed Stochastic Flow Matching for Global Probabilistic Subseasonal Forecasting of Means and Extremes}},
author = {Abdullah A. Fahad and Manmeet Singh},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.30159},
eprint = {2610.30159},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.30159},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Dynamical-Model-Informed Stochastic Flow Matching for Global Probabilistic Subseasonal Forecasting of Means and Extremes
AU - Abdullah A. Fahad
AU - Manmeet Singh
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.30159
UR - https://arxiv.org/abs/2610.30159
AB - Physics-based models provide skillful subseasonal forecasts but suffer from systematic biases, weakened teleconnections, and parameterized physics, while very large ensembles remain computationally expensive. Purely data-driven models also see too few realizations of the low-frequency modes that provide subseasonal predictability. We introduce methodname, a hybrid framework that converts modelshort subseasonal ensemble guidance into 90-member stochastic forecasts of weekly precipitation and 2 m temperature on a global 1-degree grid using a lead-aware flow-matching U-Net. Its stochastic prior initializes members with EOF perturbations conditioned on the Madden-Julian Oscillation, North Atlantic Oscillation, El Nino-Southern Oscillation, and lead week, allowing the learned flow to refine large-scale covariance rather than create it from white noise. On held-out 2021-2025 forecasts, empirical CRPS improves by 22.0% for precipitation and 25.6% for temperature in a same-initialization four-versus-four comparison, and by 32.2% and 34.0% in the 90-versus-four production comparison. Ablations across five training seeds show distinct gains from the structured prior and a learned error-variance pathway, with the prior contributing more. Verification over 120 observed-extreme cases shows positive CRPS skill, but the ensemble remains underdispersive, four-member upper quantiles are poorly resolved, and precipitation exceedance BSS is near neutral after week 1. Results support improved probabilistic skill and tail representation, but not calibrated operational rare-event probabilities. Case studies of the 2022 UK heatwave, 2023 California atmospheric rivers, and 2022 Pakistan floods illustrate the reach and limits of dynamically informed refinement.
ER -