Since the regularization of data privacy (e.g., GDPR), the effectiveness of data sharing has decreased. A promising technique to circumvent this problem is tabular data synthesis (i.e., the generation of fake tabular data that statistically resembles the original data). However, the state-of-the-art tabular data synthesis model, CTAB-GAN, fails at robustly imitating global data dependencies and underperforms when column orders get permuted. CTAB-GAN internally uses Convolutional Neural Networks (CNN) which limits the model’s performance due to a strictly non-global data perspective during iterative training phases. To address this limitation, this paper proposes FCT-GAN which leverages the Fourier Neural Operator to learn global dependencies in the frequency domain. Specifically, it enhances CTAB-GAN by replacing the CNN of the discriminator with a four-layered two-dimensional Fourier Neural Operator. As a consequence of FCT-GAN’s global nature and cross-column relation robustness, it outperforms CTAB-GAN and additionally offers the column permutation invariant property. The evaluation of FCT-GAN on five datasets shows that the generated data, remarkably resembles the real data and reveals an increase in accuracy, by up to 19% for five machine learning algorithms independent of data column order, compared to CTAB-GAN.