A forty-four-year dataset of rapeseed phenology in the Middle and Lower Yangtze River Plain of China

This study compiles and releases the first standardized rapeseed phenology period observation dataset spanning forty-four years (1981–2024) covering the core winter rapeseed production region of the Middle and Lower Yangtze River Plain in China. The data originates from systematic observations at 50 national-level agrometeorological stations across six provinces: Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, and Hunan. It provides complete records of the specific dates for each phenology stage from sowing to maturity, including eight key phenology periods: Sowing (SO), Emergence (EM), Five-leaf (FV), Bud Formation (BF), Stem Elongation (SE), Flowering (FL), Green Ripening (GR), and Maturity (MA), along with the calculated durations of six distinct growth lengths. We implemented a multi-level quality control protocol encompassing internal logical checks, statistical outlier detection, climatological validation, time series homogenization, and expert arbitration. This protocol effectively constrained data uncertainty and corrected non-climatic discontinuities. Univariate linear regression was further employed to quantify the decadal change trends of each phenology periods and growth lengths, supplemented by Kernel Density Estimation (KDE) to characterize their probability distribution features. The final dataset is presented as structured tables (in xlsx format) and high-resolution diagnostic plots (including trend and density plots), with a total volume of approximately 470 MB, systematically organized by province and station. This dataset systematically addresses the lack of long-term, standardized rapeseed phenology data products for this region. It provides an indispensable, high-quality empirical foundation for in-depth investigations into the nonlinear response mechanisms of overwintering crop phenology to climate warming, improving the parameterization and validation of crop process models, and assessing climate change impacts to formulate regional adaptive management strategies.