Goal representation affects the performance of Hierarchical Reinforcement Learning (HRL) algorithms by decomposing complex problems into easier subtasks. Recent studies show that representations that preserve temporally abstract environment dynamics are successful in solving difficult problems with theoretical guarantees for optimality. These methods however cannot scale to tasks where environment dynamics increase in complexity. On the other hand, other efforts have tried to use spatial abstraction to mitigate the previous issues. Their limitations include scalability to high dimensional environments and dependency on prior knowledge. In this work, we propose a novel three-layer HRL algorithm that introduces, at different levels of the hierarchy, both a spatial and a temporal goal abstraction. We provide a theoretical study of the regret bounds of the learned policies. We evaluate the approach on complex continuous control tasks, demonstrating the effectiveness of spatial and temporal abstractions learned by this approach.