Agricultural irrigation, as an important practice to protect crops from drought and promote grain yield, has a long history in China. A timely and precise dataset about the extent and dynamics of irrigated areas is necessary for water allocation and agricultural management but is scarce in China. Here we developed annual irrigated cropland maps across China (IrriMap_CN) at 500-m resolution from 2000 to 2019, using MODIS data, machine-learning method, and Google Earth Engine platform. First, we generated annual nationwide training samples by strictly screening the existing irrigation maps downscaled from the statistical data. Second, we implemented locally adaptive random forest classifiers in 511 nominal 1° × 1° grid cells across China with MODIS vegetation indices, climatic factors, and topography variables. Third, we conducted nationwide pixel-wise validation of the IrriMap_CN using independent samples. The validation results based on more than 3000 ground truth points revealed that IrriMap_CN had high accuracies ranging from 77.2% to 85.9%. The time series of IrriMap_CN detected substantial expansion of irrigated areas in Xinjiang and Heilongjiang (more than 50% in total) and pronounced decreases in Sichuan, Jiangsu, and Hebei. The analyses of irrigation frequency, start time, and end time implied that North China Plain was the most intensive irrigated area; but the irrigation area showed a decreasing trend since 2000, consistent with the reduced agricultural water consumption. The annual irrigation datasets allow us to understand the spatiotemporal dynamics of irrigated croplands in China and are expected to contribute to the improvement of earth system models and facilitate sustainable agricultural water management.