{"ID":2823389,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.00915","arxiv_id":"2601.00915","title":"Latent-Constrained Conditional VAEs for Augmenting Large-Scale Climate Ensembles","abstract":"Large climate-model ensembles are computationally expensive; yet many downstream analyses would benefit from additional, statistically consistent realizations of spatiotemporal climate variables. We study a generative modeling approach for producing new realizations from a limited set of available runs by transferring structure learned across an ensemble. Using monthly near-surface temperature time series from ten independent reanalysis realizations (ERA5), we find that a vanilla conditional variational autoencoder (CVAE) trained jointly across realizations yields a fragmented latent space that fails to generalize to unseen ensemble members. To address this, we introduce a latent-constrained CVAE (LC-CVAE) that enforces cross-realization homogeneity of latent embeddings at a small set of shared geographic 'anchor' locations. We then use multi-output Gaussian process regression in the latent space to predict latent coordinates at unsampled locations in a new realization, followed by decoding to generate full time series fields. Experiments and ablations demonstrate (i) instability when training on a single realization, (ii) diminishing returns after incorporating roughly five realizations, and (iii) a trade-off between spatial coverage and reconstruction quality that is closely linked to the average neighbor distance in latent space.","short_abstract":"Large climate-model ensembles are computationally expensive; yet many downstream analyses would benefit from additional, statistically consistent realizations of spatiotemporal climate variables. We study a generative modeling approach for producing new realizations from a limited set of available runs by transferring...","url_abs":"https://arxiv.org/abs/2601.00915","url_pdf":"https://arxiv.org/pdf/2601.00915v1","authors":"[\"Jacquelyn Shelton\",\"Przemyslaw Polewski\",\"Alexander Robel\",\"Matthew Hoffman\",\"Stephen Price\"]","published":"2026-01-01T05:23:28Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Variational Autoencoder\"]","has_code":false}