<p>Engineering systems operating under stochastic degradation and variable environmental conditions require maintenance strategies that move beyond stationary assumptions and purely degradation-driven decision rules. This paper proposes a unified simulation-based framework for the analysis of environment-aware adaptive condition-based maintenance (CBM) strategies, in which system degradation is modeled by a drifted Wiener process and operating conditions evolve according to a finite-state Markov chain. Two environment-gated maintenance policies are investigated: a strategy that authorizes preventive maintenance exclusively under benign environmental conditions, and an alternative strategy that prioritizes intervention during harsh operating regimes once degradation exceeds a prescribed threshold. The primary scientific contribution of this work lies in the systematic and statistically grounded comparison of alternative environment-aware CBM decision logics within a common Monte Carlo simulation framework, enabling consistent multi-criteria performance evaluation under identical stochastic assumptions. Extensive numerical experiments are conducted to assess expected lifecycle cost, cost dispersion, mission-oriented reliability, and operational downtime. The results demonstrate that conditioning maintenance decisions on environmental state has a pronounced impact on economic and availability-related performance. While mission-success probabilities remain uniformly low under the selected parameter configuration and planning horizon (reflecting a severe degradation regime and a strict mission-oriented reliability definition) formal statistical validation confirms that observed similarities in reliability across strategies are not statistically significant. In contrast, substantial differences are observed in cost variability and downtime, underscoring the practical value of environment-gated maintenance policies for managing cost–risk–availability trade-offs. Overall, the findings provide clear evidence that environment-aware adaptive CBM strategies offer meaningful advantages over static approaches when evaluated through appropriately defined performance metrics in uncertainty-intensive operating contexts.</p> Graphical abstract <p>This study proposes an environment-aware adaptive maintenance framework for stochastic degradation systems. A Wiener process with drift models degradation, while a Markov chain captures environmental variability (normal vs. harsh states). Two adaptive policies are compared: (i) deferring preventive maintenance until nominal environmental conditions, and (ii) intervening in harsh environments once a critical threshold is reached. Monte Carlo simulations evaluate life-cycle cost, reliability, and downtime. Results highlight the trade-offs and advantages of integrating environmental awareness into Condition-Based Maintenance (CBM), offering a pathway to more resilient and cost-effective asset management in uncertain operating regimes.</p> <p></p>