The purpose of image arbitrary style transfer is to apply a given artistic or photorealistic style to a target content image. While existing methods can effectively transfer style information, the variability in color brightness features between the style and content images can significantly impact the quality of the style transfer. Specifically, traditional arbitrary style transfer relies on an encoder-decoder architecture of spatial-aware neural networks operating in the RGB domain, which typically preserves only local color brightness information and lacks a global color perception compared to the wavelet frequency domain. In this paper, we address the issue of insufficient global color perception in images from the perspective of image enhancement. We propose Global Color-aware Arbitrary Style Transfer with Discrete Wavelet Transform (GCAST), which achieves a balance between the global color characteristics of style images and the local details of content images. Our approach involves an encoder that integrates preliminary feature encoding through cross-layer attention fusion, while the decoder modifies frequency features using a wavelet-based adjustment block. To maintain a balance between content and style in the stylized images, we design a comprehensive loss function that incorporates multiple restoration loss, double style loss, and noise regularization loss. Extensive experiments comparing our method with state-of-the-art techniques demonstrate that our proposed model generates stylized images with superior results in terms of global color perception and human perceptual studies.