This study develops an explainable variational autoencoder (VAE) framework to efficiently generate high-fidelity local circulation patterns in Taiwan, ensuring an accurate representation of the physical relationship between generated local circulation and upstream synoptic flow regimes. Large ensemble semi-realistic simulations were conducted using a high-resolution (2 km) model, TaiwanVVM, where critical characteristics of various synoptic flow regimes were carefully selected to focus on the effects of local circulation variations. The VAE was constructed to capture essential representations of local circulation scenarios associated with the lee vortices by training on the ensemble dataset. The VAE’s latent space effectively captures the synoptic flow regimes as controlling factors, aligning with the physical understanding of Taiwan’s local circulation dynamics. The critical transition of flow regimes under the influence of southeasterly synoptic flow regimes is also well represented in the VAE’s latent space.This indicates that the VAE can learn the nonlinear characteristics of the multiscale interactions involving the lee vortex. The latent space within VAE can serve as a reduced-order model for predicting local circulation using synoptic wind speed and direction. This explainable VAE ensures the accurate predictions of the nonlinear characteristics of multiscale interactions between synoptic flows and the local circulation induced by topography, thereby accelerating the assessments under various climate change scenarios.