MUNDO: A Multiresolution Generative Framework for Temporal Downscaling

Dynamical systems are often observed at coarse temporal resolution through sparse states or temporal aggregates, leaving the underlying fine-scale trajectory unresolved. We present MUNDO (Multiresolution UNified DOwnscaling), a generative framework for temporal downscaling. MUNDO builds on engression, which trains a conditional sampler with a strictly proper scoring rule and generates a sample in a single network evaluation, without iterative diffusion-based sampling. MUNDO extends this to a hierarchy of resolutions by factorizing the observation operator into successive coarsenings. The resulting conditionals are then sampled in reverse, with each level generating only the degrees of freedom lost between adjacent resolutions. We evaluate MUNDO on two-scale Lorenz-96, four 2D physical systems, and multivariate ERA5 reanalysis over Europe, for both temporal interpolation and disaggregation. Across datasets and tasks, MUNDO generally outperforms flow-based baselines. Single-step MUNDO achieves strong predictive skill, while recursive MUNDO generally improves ensemble calibration, with better spread-skill agreement and predictive coverage.