PatchShot: Rare Precipitation Events Are Encoded Yet Hidden from Standard Readouts

Rare precipitation events are difficult to recognize under severe label scarcity. We show that frozen 3DMAE radar representations already encode discriminative evidence for high-threshold events, yet standard readouts fail to recover it, revealing a representation accessibility gap. We introduce PatchShot, a case-disjoint inductive few-shot framework for parameter-efficient representation re-access. PatchShot freezes the weather encoder, learns a lightweight adapter through support-supervised contrastive learning, constructs support-derived prototypes, and calibrates the decision threshold using support data alone. Query patch scores are aggregated into case-level predictions without using query labels. We further propose PatchShot-Env, which conditions the re-access adapter on matched ERA5 low-mode context through lightweight expert routing. Across precipitation thresholds and support budgets, PatchShot substantially improves over the frozen QPE output and direct prototype readouts, while matched environmental routing provides further gains and shuffled-context controls weaken them. Probe and intervention analyses confirm that the improvement reflects recovered access to encoded rare-event evidence rather than encoder retraining.