Strategic long-term planning of mountain forests in the European Alps requires a balancing act between sustaining forest biodiversity and ecosystem services (BES) and mitigating disturbance risks, particularly under climate change. In this context, close-to-nature forestry (CNF) is considered an effective strategy. However, it remains unclear whether current CNF strategies sufficiently reduce forests’ predisposition to climate-change-induced shifts in disturbance regimes, including the occurrence of novel disturbances such as forest fires. To address this complexity, we integrated the forest gap model ForClim with predisposition assessments for fire, bark beetle, and windthrow disturbances – as well as evaluations of BES provision – into a decision support system (DSS). Simulations were conducted for a forest enterprise in the Central Swiss Alps, covering a large elevation gradient, under three climate scenarios (historical, SSP2-4.5, and SSP5-8.5) and six management strategies, including CNF variants with different management intensities and climate-adapted approaches. Our results indicate that climate change will dynamically alter disturbance predisposition across elevation gradients: For example, under severe warming (SSP5-8.5), long-term reductions in stand-related disturbance predisposition occurred at lower elevations due to declining forest productivity, while predisposition increased at higher elevations with improved growing conditions. CNF emerged as a balanced approach for reducing predisposition to bark beetle infestation and windthrow while maintaining BES. However, CNF promoted stand characteristics that increased stand-related predisposition to forest fires. Our results further show that increasing management intensity generally reduces stand-related disturbance predisposition but can also lead to trade-offs, such as reduced BES provision. We conclude that proactively reducing disturbance predisposition may involve short-term trade-offs regarding BES provision but may be crucial to avoid larger, long-term BES losses caused by severe disturbances. Our study underscores the need for decision support systems to support informed decision-making in mountain forest management.