Eta-learning generates plausible extreme events without paired extreme examples

What changed. Researchers constrained a learned low-to-high-resolution map so an extremeness-relevant observable matches known statistics, with an optimal-transport justification and precipitation-downscaling demonstration.

Technique / discovery. Distribution-constrained learning over an extremeness observable with optimal-transport theory.

Why it matters. Rare events are high consequence but poorly represented in paired training data; tail information can be injected as a distributional constraint.

Application. Climate downscaling, hazard modeling, reliability engineering, and rare-failure simulation

Evidence maturity. Peer-reviewed (source tier A)

Identified bottleneck. Observable choice, sparse tail-statistic estimation, physical consistency, nonstationarity, and probability calibration remain unresolved.

Caveat / evidence note. Peer-reviewed Nature Communications paper. Matching a tail statistic does not prove calibrated probabilities under process or climate shift.

Review status. Reviewed. User requested: Yes.

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