We produced a 10 m maize yield dataset for Northeast China covering 2019–2024 by combining process-based crop simulations with deep learning. The framework avoids the need for field yield labels during training while maintaining good accuracy against independent observations. The dataset captures both regional yield patterns and fine-scale field variability, supporting agricultural monitoring and management.