Development of a Labeled Dataset for Convection Initiation Events over China’s Central and Eastern Mainland During the Warm Season

Advances in artificial intelligence models offer promising approaches for intelligent convection initiation (CI) identification—a critical step in severe weather nowcasting—thereby driving demand for high-quality, long-term labeled datasets. Accordingly, this study develops a CI identification technique using quality-controlled, gridded composite reflectivity data from the weather radar and CMA global atmospheric reanalysis wind data over central-eastern China (2018–2023) to construct a labeled dataset. The proposed CI identification method integrates a “forward-time search and backward-time verification” strategy, which involves three key steps: screening grid points with absent or weak convection; monitoring these points for convective development within 30 min; and finally, confirming the first occurrence of convection. Additionally, quality control is applied to eliminate the influence of outliers and anomalous radar data. The resulting dataset constructed from 829 severe convective processes comprises ~25.6 million grid points labeled for CI occurrences at one or more lead times of 10, 20, or 30 min to resolve spatiotemporal evolution. Of these, 71.90% are accompanied by surface weather phenomena. This study provides a reliable dataset to support the learning of intelligent identification and nowcasting models for CI events. Furthermore, based on this dataset, the spatiotemporal distribution characteristics of warm-season CI over central-eastern China are delineated.