Multimode processes may be operating under different statuses, revealing different process characteristics and variations. It can be an interesting issue to check how the process variations change from one mode to another and thus how they can be used for multimode analysis and monitoring. In the present work, a between-mode relative analysis algorithm is proposed to explore the between-mode relationship. Comprehensive subspace decomposition is performed in each mode regarding their relative changes from the reference mode and influences on process monitoring. Each mode is separated into three different systematic subspaces and one residual subspace based on the between-mode changes of process variations, including the increased part, the decreased part, and the unchanged part. These different variations are then modeled respectively and different monitoring statistics are defined for online monitoring to identify mode affiliation and detect the fault status. By separating and monitoring different types of between-mode relative variations, the proposed method can not only efficiently distinguish different modes and detect faults but also offer enhanced process understanding. Theoretical support is framed and the related statistical characteristics are analyzed. Its feasibility and performance are illustrated with multimode data generated from the Tennessee Eastman (TE) benchmark process.