This study investigates how the location-routing decisions of the electric vehicle (EV) DC Fast Charging (DCFC) charging stations are impacted by the ambient temperature.Electric vehicles are expected to contribute significantly to the delivery mission of logistic companies in the future. In an EV delivery logistics network equipped with DCFC stations, this study investigates how the location strategy of DCFC charging stations and the routing plan of a fleet of EVs are impacted by the ambient temperature. We formulated this problem as a mixed-integer linear programming model that captures the realistic charging behavior of the DCFC’s in association with the ambient temperature and their subsequent impact on the EV charging station location and routing decisions. Two innovative heuristics are proposed to solve this challenging model in a realistic test setting, namely, the two-phase Tabu Search-modified Clarke and Wright algorithm and the Sweep-based Iterative Greedy Adaptive Large Neighborhood algorithm. We use Fargo city in North Dakota as a testbed to visualize and validate the algorithm performances. The results clearly indicate that the EV DCFC charging station location decisions are highly sensitive to the ambient temperature, the charging time, and the initial state-of-charge. The results provide numerous managerial insights for decision-makers to efficiently design and manage the DCFC EV logistic network for cities that suffer from high-temperature fluctuations.