Fourier Neural Operators (FNO) have emerged as a powerful paradigm for solving partial differential equations (PDEs) by learning discretization-invariant solution operators that effectively capture global flow dependencies. However, due to the global nature of Fourier basis functions and the truncation of high-frequency modes, standard FNO often struggle to resolve fine-grained local features and discontinuities, limiting their fidelity in capturing detailed flow structures. To address this limitation, this study introduces a Hybrid FNO-CNN architecture for airfoil flow field prediction. The proposed framework employs a parallel dual-branch structure: an FNO branch responsible for resolving global flow patterns in the frequency domain, and a Convolutional Neural Network (CNN) branch dedicated to the refinement of local spatial details. A key innovation is the integration of a learnable fusion mechanism that dynamically weights the contributions of both branches, enabling the model to synergize global coherence with local precision during the learning process. The model was evaluated on a comprehensive dataset of airfoils under a wide range of inflow conditions. Numerical experiments demonstrate that this hybrid approach achieves superior predictive accuracy, reducing the relative error by approximately 5% compared to the standard FNO. Notably, this accuracy improvement incurs negligible computational overhead, maintaining training efficiency comparable to that of the standard FNO under identical experimental settings. Furthermore, the proposed model offers a computational speedup of 3 to 5 orders of magnitude over traditional CFD solvers. By effectively integrating spectral information with local convolutional receptive fields, the proposed architecture significantly enhances the prediction of velocity and pressure fields, offering a robust and efficient surrogate model for aerodynamic design optimization.