<p>With the rapid advancement of deep learning, remote sensing image change detection (CD) has made significant progress. The complementary strengths of convolutional neural networks (CNNs) and Transformers have attracted considerable attention. This has prompted researchers to explore CNN-Transformer parallel architectures for CD tasks. However, existing methods often rely on spatial-domain operations, such as convolution and pooling, to integrate local and global features, which can result in the loss of fine-grained details, leading to incomplete detection of small changes and blurred boundaries in change regions. Additionally, many methods employ a single strategy for difference extraction, limiting their ability to model temporal dependencies and making them susceptible to irrelevant environmental variations, which can cause pseudo-changes. To address these challenges, we propose a Hybrid CNN-Transformer Network with Difference Enhancement and Frequency Fusion (HCTFNet). HCTFNet introduces a Temporal Feature Fusion Module (TFFM) that efficiently extracts difference features via a dual-branch operation, while integrating an attention mechanism to highlight actual changes and suppress irrelevant noise. Furthermore, the Hybrid CNN-Transformer Fusion (HCTF) module extracts both local and global features and applies frequency-domain processing to enhance the interaction between local and global features, thereby preserving fine-grained spatial details more effectively. Extensive experiments conducted on three publicly available benchmark datasets demonstrate that HCTFNet achieves superior CD performance compared to existing mainstream methods.</p>