Abstract
Understanding how surface climate variables interact directionally remains challenging, particularly in complex terrain environments where nonlinear dynamics, terrain heterogeneity, and seasonal regime shifts limit the applicability of conventional correlation‐based analyses. This study develops a multivariate, cross‐station Transfer Entropy (TE) framework to diagnose directional climate interactions among wind speed, temperature, surface pressure, and relative humidity across five surface stations in Hong Kong. Daily observations from 1998–2018 are analysed separately for cold (October–April) and warm (May–September) monsoon regimes. The results show that directional asymmetry is consistently present across all variable pairs, stations, and seasons. However, both the magnitude and structure of information transfer exhibit strong seasonal and terrain dependence. During the cold season, interactions are spatially heterogeneous, with localised dominance of pressure‐to‐humidity coupling and temperature‐driven effects at high‐elevation sites. In contrast, the warm season displays a network‐wide amplification of pressure‐to‐humidity interaction. In addition, the inter‐station coupling of individual climate variables is also systematically diagnosed. Overall, the proposed approach robustly resolves spatially distributed and regime‐dependent information transfer, thus enabling identification of dominant dynamic interaction patterns within complex climate systems.