Marchuk-S2S: Compact Latent Flow Matching for Six-Hourly Subseasonal Ensemble Weather Forecasting

Subseasonal forecasts two to six weeks ahead must draw predictability from slowly evolving modes such as the Madden–Julian Oscillation (MJO) after the memory of the initial state has faded, and are therefore issued as large ensembles. Diffusion- and flow-based weather models generate sharp six-hourly ensembles but have been evaluated mainly in the medium range. We introduce Marchuk-S2S, a 276M-parameter Transformer trained with conditional flow matching in the latent space of a frozen pretrained weather autoencoder. Conditioned on one atmospheric state and time-of-year labels, it jointly samples multi-day chunks of six-hourly states; a single checkpoint trained on 1- to 8-day chunks is composed autoregressively to 48 days, without lead-time-specific networks or gridded climatology. A 50-member, 48-day ensemble takes 9.7 minutes on one H100 GPU, $7.6\times$ faster than LaDCast. Over 2019–2021, Marchuk-S2S is competitive with FuXi-S2S and the 51-member ECMWF ensemble in weekly anomaly correlation and MJO skill. Training on six-hourly states and aggregating to daily means lowers CRPS on all five evaluated fields compared with training on daily means. Treating each call as a stochastic transition, we show that composing two chunks keeps the ensemble scale of one direct chunk of twice the length and lowers its CRPS, without a consistency loss.