This paper addresses the issue of how to account for short‐term temporal variability of renewable energy sources and power demand in long‐term climate change mitigation scenarios in energy‐economic models. An approach that captures in a stylized way the major challenges to the integration of variable renewable energy sources into power systems has been developed. As a first application this approach has been introduced to REMIND‐D, a hybrid energy‐economy model of Germany. An approximation of the residual load duration curve is implemented. The approximating function endogenously changes depending on the penetration and mix of variable renewable power. The approach can thus be used to account for variability and correlations between different sources of renewable supply and power demand within the intertemporal optimization of long‐term (energy system) investment decisions in climate change mitigation scenarios. Moreover, additional constraints are introduced to account for flexibility requirements concerning loadfollowing and ancillary services. The parameterization is validated with MICOES a highly resolved dispatch model. Model results show that significant changes are induced when the new residual load duration curve methodology is implemented. With variability, scenarios show that the German power sector is no longer fully decarbonized because natural gas combined‐cycle plants are built to complement renewable energy generation. The mitigation costs increase by about 20% compared to a model version in which variability is not taken into account.