AirportMIST: A Multimodal Benchmark for Airport Weather-State Forecasting with Rare Adverse Conditions

Airport operations depend on timely forecasts of operationally meaningful local states, including rare adverse conditions such as thunderstorms and degraded flight conditions, yet existing weather and aviation benchmarks primarily focus on continuous variables, gridded atmospheric fields, or individual hazards. We introduce AirportMIST, a multimodal benchmark for multi-airport, multi-horizon categorical airport weather-state forecasting. AirportMIST covers 37 major civil airports in China during JJA 2020 and JJA 2025 and integrates high-frequency AWOS observations, irregular METAR reports, regional WAFS forecast fields in GRIB format, and static airport metadata. It defines precipitation/thunderstorm and flight-condition forecasting at 6-, 12-, and 24-hour horizons, together with chronological splits, causality-preserving multimodal alignment, and rare-event evaluation. We further provide MISTNet as a reproducible reference model for heterogeneous local and regional inputs. Experiments with operational, statistical, and time-series baselines reveal that rare-event ranking, multiclass argmax detection, and operational forecast behavior can lead to different conclusions: several learned models retain nonzero AP while making no Thunder predictions under multiclass argmax, whereas risk-aware TAF forecasts operate at a substantially higher predicted-positive rate. These results motivate evaluating airport weather-state forecasting beyond aggregate classification performance. AirportMIST provides a standardized and reproducible foundation for research on multimodal airport weather-state forecasting and rare-event prediction. Our code is available at https://anonymous.4open.science/r/MIST-1FA1/.