[Objective] The static design of urban drainage infrastructure，which separates green-gray infrastructure （GGI） planning from dynamic operational strategies，limits its effectiveness in mitigating flood peaks under extreme rainfall events. This study develops a multi-objective optimization model that integrates real-time control （RTC） with GGI to enhance both peak shaving capacity and cost-effectiveness. The primary objective is to systematically evaluate how RTC influences the cost-benefit relationship and optimal configuration of GGI under various climate scenarios. [Methods] A representative urban drainage catchment in Shenzhen，China，covering an area of 85.6 hectares，is selected as the case study. A coupled TVGM-SWMM hydrological model is established to simulate rainfall-runoff and pipe network processes. Storage tanks are real-time controlled through a predictive fuzzy logic control （PFLC） method combined with a target flow allocation strategy，enabling coordinated dynamic regulation. The Non-dominated Sorting Genetic Algorithm II （NSGA-II） is applied to minimize life cycle cost and maximize life cycle comprehensive environmental benefit. Decision variables include： ratio of green infrastructure coverage area，total storage volume of gray infrastructures，and RTC parameters. Future climate scenarios are generated using the change factor methodology applied to ten CMIP6 GCMs dataset，producing design storms for three shared socioeconomic pathways （SSP126，SSP245，SSP585） at three future time horizons （2030，2040，2050）. This comprehensive framework allows systematic assessment of RTC impacts on GGI performance across current and future climate conditions. [Results] （1） The integration of RTC significantly enhances the peak shaving capability of GGI under extreme rainfall conditions. For the 100-year design storm with 24-hour duration，RTC achieves a peak flow reduction of 38.5% at the catchment outlet，reducing discharge from 24.98 to 15.32 m³/s. The advantage of RTC becomes increasingly pronounced as rainfall intensity and duration increase. （2） From an economic perspective，coupling RTC with GGI substantially improves cost-effectiveness. To achieve equivalent peak reduction targets of 20%，40%，and 60%，the life cycle cost of the system is reduced by 32%，47%，and 39%，respectively，compared to static control scenarios. The investment threshold required to generate positive environmental returns is markedly lowered，and the diminishing marginal utility effect is mitigated. As rainfall intensity increases across SSP scenarios （SSP585>SSP245>SSP126），environmental benefits naturally decline，but RTC achieves incremental benefit improvements of 6%-13% at equivalent cost levels compared to static control. （3） RTC also alters the optimal configurations of infrastructures and their investment structure. Under low-cost constraints （<0.25 million CNY/ha），RTC enables gray infrastructure volumes up to 8×10³ m³ with green infrastructure coverage below 1%，whereas static control relies predominantly on green infrastructure （coverage 17%-28%） with gray volumes below 1×10³ m³. Analysis of contribution ratios confirms that under RTC，gray infrastructure's benefit contribution consistently exceeds its cost contribution，whereas under static control the opposite pattern prevails. Despite this shift，green infrastructure retains significant investment share （i.e.，25%-75%） across all climate scenarios，indicating a complementary synergy rather than substitution between green and gray components. [Conclusions] Real-time control significantly enhances both the flood mitigation performance and economic efficiency of green-gray infrastructure systems under current and future climate conditions. By dynamically coordinating distributed storage facilities，RTC strengthens the marginal contribution of gray infrastructure，reduces the investment threshold for achieving environmental gains，and optimizes the synergy between green and gray components. The finding that green infrastructure maintains substantial investment share under RTC underscores its continuing importance in source control and runoff reduction，which alleviates downstream storage demand and indirectly enhances RTC effectiveness. This complementary relationship suggests that RTC does not replace green infrastructure but rather enables more efficient utilization of the integrated system. These findings demonstrate that integrating RTC with GGI offers a promising pathway to improve urban flood resilience and investment returns. The proposed modeling framework，which couples hydrological simulation，multi-objective optimization，and climate scenario analysis，provides a valuable decision-support tool for climate-adaptive design and operation of urban drainage systems. Future research should explore the application of this framework to larger spatial scales，incorporate water quality objectives，and investigate real-time implementation challenges including sensor networks，communication systems，and control reliability.