We'll present a high-level framework for producing parallel and efficient adaptive mesh refinement code on GPU-accelerated supercomputers. AMR methods reduce computational requirements of problems by increasing resolution for only areas of interest. However, in practice, efficient AMR implementations are difficult, considering that the mesh hierarchy management must be optimized for the underlying hardware. Architecture complexity of GPUs can render efficient AMR to be particularity challenging in GPU-accelerated supercomputers. We'll present a compiler-based, high-level framework that can automatically transform serial uniform mesh code annotated by the user into parallel adaptive mesh code optimized for GPU-accelerated supercomputers. We show experimental results on three production applications. The speedups of code generated by our framework are comparable to hand-written AMR code while achieving good strong and weak scaling up to 3,640 GPUs.