Highlights What are the main findings? A CNN-Informer hybrid model is developed to integrate multi-source spatiotemporal data (remote sensing, meteorological, soil, and historical yields), combining convolutional local feature extraction with ProbSparse attention for efficient long-range dependency modeling. Comparative experiments demonstrate that the proposed model significantly outperforms representative baselines (LSTM, CNN, Transformer, Informer, and XGBoost), achieving R2 = 0.82, RMSE = 0.31 t/ha, MAE = 0.21 t/ha, and MAPE = 10.33%, representing an average improvement of 10-35% across metrics. What are the implications of the main findings? Cross-county fivefold validation and feature ablation confirm strong spatial generalization and robustness, indicating the model's applicability for county-level yield prediction in arid and semi-arid regions. Analysis of feature contributions highlights the dominant role of historical yield and remote sensing indices, while soil and meteorological variables improve spatial differentiation, providing actionable insights for precision crop management and data-driven decision-making.Highlights What are the main findings? A CNN-Informer hybrid model is developed to integrate multi-source spatiotemporal data (remote sensing, meteorological, soil, and historical yields), combining convolutional local feature extraction with ProbSparse attention for efficient long-range dependency modeling. Comparative experiments demonstrate that the proposed model significantly outperforms representative baselines (LSTM, CNN, Transformer, Informer, and XGBoost), achieving R2 = 0.82, RMSE = 0.31 t/ha, MAE = 0.21 t/ha, and MAPE = 10.33%, representing an average improvement of 10-35% across metrics. What are the implications of the main findings? Cross-county fivefold validation and feature ablation confirm strong spatial generalization and robustness, indicating the model's applicability for county-level yield prediction in arid and semi-arid regions. Analysis of feature contributions highlights the dominant role of historical yield and remote sensing indices, while soil and meteorological variables improve spatial differentiation, providing actionable insights for precision crop management and data-driven decision-making.Abstract Oilseed flax (Linum usitatissimum, L.) is an important specialty oilseed crop cultivated in arid and semi-arid regions, where timely, accurate yield prediction is crucial for regional oilseed security and agricultural decision-making. To address the lack of robust county-level yield prediction models for oilseed flax, this study proposes a CNN-Informer hybrid framework that integrates convolutional neural networks (CNNs) with the Informer architecture to model multi-source spatio-temporal data. Unlike conventional Transformer-based approaches, the proposed framework combines CNN-based local temporal feature extraction with the ProbSparse attention mechanism of Informer, enabling the efficient modeling of long-range temporal dependencies across multiple years while reducing the computational burden of attention-based time-series modeling. The model incorporates multi-source inputs, including remote sensing indices (NDVI, EVI, SAVI, KNDVI), TerraClimate meteorological variables, soil properties, and historical yield records. Comprehensive experiments conducted at the county level in Gansu Province, China, demonstrate that the CNN-Informer model consistently outperforms representative machine learning and deep learning baselines (Transformer, Informer, LSTM, and XGBoost), achieving an average performance of R2 = 0.82, RMSE = 0.31 t/ha, MAE = 0.21 t/ha, and MAPE = 10.33%. Results from feature ablation and historical yield window analyses reveal that a three-year historical yield window yields optimal performance, with remote sensing features contributing most strongly to predictive accuracy, while meteorological and soil variables enhance spatial adaptability under heterogeneous environmental conditions. Model robustness was further verified through fivefold county-based spatial cross-validation, indicating stable performance and strong generalization capability in unseen regions. Overall, the proposed CNN-Informer framework provides a reliable and interpretable solution for county-level oilseed flax yield prediction and offers practical insights for precision management of specialty crops in arid and semi-arid regions.