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SimCast-S2S: A Computationally Efficient Diffusion Model for Subseasonal Precipitation Forecasting — arXiv:2608.26594

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.26594
License (as archived): CC0-1.0
Archived: 2026-10-17T16:09:45.832Z
SHA-256 of the archived text: 46cdc7042254ae8af17bda5ffb66e99f98409c335e98d15df1917747c7e02180
Stable URL of this page: https://metraipolis.com/documento/dc_bb2dd39a088f

How to cite

SimCast-S2S: A Computationally Efficient Diffusion Model for Subseasonal Precipitation Forecasting — arXiv:2608.26594 — arXiv (en), https://arxiv.org/abs/2608.26594 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bb2dd39a088f (Metraipolis archive dc_bb2dd39a088f, sha256 46cdc7042254ae8af17bda5ffb66e99f98409c335e98d15df1917747c7e02180)

BibTeX

@misc{dcbb2dd39a088f,
  title = {SimCast-S2S: A Computationally Efficient Diffusion Model for Subseasonal Precipitation Forecasting — arXiv:2608.26594},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.26594},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bb2dd39a088f; sha256 46cdc7042254ae8af17bda5ffb66e99f98409c335e98d15df1917747c7e02180},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.26594. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 effective sampling from the underlying conditional distribution. Second, since generating large probabilistic ensembles is computationally costly in physical space, SimCast-S2S instead operates in a compact latent space learned by variational autoencoders (VAEs), enabling efficient large-ensemble generation. Third, diffusion models typically require large training datasets; SimCast-S2S overcomes this via transfer learning with low-rank adaptation (LoRA), pretraining on large ensembles of climate simulations before fine-tuning on limited reanalysis data. On reanalysis data, SimCast-S2S outperforms deep learning baselines, including convolutional neural networks and U-Net architectures. Notably, despite using only a subset of atmospheric input variables and no post-processing, bias correction, or calibration, SimCast-S2S remains competitive with, and in many aspects outperforms, state-of-the-art operational systems such as the ECMWF-S2S baseline. These results indicate that latent generative modeling combined with simulation-to-reanalysis transfer learning offers an efficient and scalable path toward data-driven probabilistic S2S precipitation forecasting.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-28 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.26594 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T15:34:49.914Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-28?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).