The machine learning dataset of thunderstorm gale in the Sichuan Basin

The data were obtained from the China Meteorological Administration (CMA) and consist of hourly maximum wind speed and lightning observations that have undergone rigorous quality control. Forecast predictors were selected from the ECMWF (European Centre for Medium-Range Weather Forecasts) forecast products issued at 08:00 Beijing Time, including 115 atmospheric parameters such as temperature, wind speed, and Convective Available Potential Energy (CAPE), with horizontal resolutions of 0.125° × 0.125° (surface) and 0.25° × 0.25° (upper air). Data processing included: (1) unifying the temporal resolution to 3-hour intervals; (2) labeling samples as positive if at least one thunderstorm gale occurred within a 3-hour window; (3) feature extraction; and (4) matching gridded forecast data to observation stations using bilinear interpolation.constructs eight negative sample datasets (Prop1–Prop8) with varying positive-to-negative ratios ranging from 1:1 to 1:8.three meteorologically meaningful temporal sampling schemes were further designed: N1 (traditional synchronous sampling), N2 (sampling 12 hours in advance for comparison), and N3 (random sampling within the 12-hour lead time).All data are standardized using the min max method and subjected to random shuffling preprocessing