SimCast-S2S: A Computationally Efficient Diffusion Model for Subseasonal Precipitation Forecasting
Subseasonal-to-seasonal (S2S) precipitation forecasting has substantial financial and societal impact, yet remains challenging because of weak predictive signals, high associated uncertainty, and the computational cost of operational systems, which constrains simulation fidelity. We introduce SimCast-S2S, a generative latent-diffusion framework for probabilistic S2S precipitation forecasting that addresses three major bottlenecks in data-driven prediction. First, because S2S prediction requires uncertainty quantification rather than only deterministic point forecasts, SimCast-S2S is the first data-driven system that uses a diffusion-based generative pipeline for S2S prediction, enabling e...
| Authors | Hiep V. Dang, Antonios Mamalakis |
|---|---|
| Primary category | cs.LG |
| Published | 27 August 2026 |
| Last updated at source | 25 September 2026 |
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