Conformalized Weather Forecasting: Distribution-Free Coverage Guarantees for Neural Weather Models

Conformal calibration of a weather field must specify what is covered: an individual location, an average fraction of grid cells, or a field-level event. We study the event that at least 90% of unweighted grid cells lie inside their forecast intervals. A spatial order-statistic score gives the standard finite-sample split-conformal guarantee for this event under exchangeable forecast–verification pairs. A chronological Stormer audit instead tests where this assumption can fail: using 240 calibration cases from 2018–2019 and 336 test cases from 2020–2022, global calibration attains a 71.4% field-event rate. Six latitude bands attain 97.0% with mean width 4.27 K, but their worst-band score-event rate is only 48.2%; aggregate field coverage does not imply simultaneous regional validity. We also audit label timing using the same saved forecasts. For a fixed three-band rolling window, increasing an assumed label delay from zero to 90 days reduces field-event coverage from 98.2% to 89.6%, while width changes little. These are retrospective sensitivities using final archived targets, not recovered ERA5T vintages. The contribution is an explicit coverage-target and label-availability audit; operational validity under temporal shift remains outside the exchangeable guarantee.