Parallel multigrid is widely used as preconditioners in solving large-scale sparse linear systems. However, the current multigrid library still needs more satisfactory performance for structured grid problems regarding speed and scalability. To this end, we design and implement StructMG, a fast and scalable multigrid that constructs hierarchical grids automatically based on the original matrix. As a preconditioner, StructMG can achieve both low cost per iteration and good convergence. Two idealized and five real-world problems from four application fields, including radiation hydrodynamics, petroleum reservoir simulation, numerical weather prediction, and solid mechanics, are evaluated on ARM and X86 platforms. In comparison to hypre's multigrid preconditioners, StructMG achieves the fastest time-to-solutions in all cases with average speedups of 17.6x, 5.7x, 4.6x, 8.5x over SMG, PFMG, SysPFMG, and BoomerAMG, respectively. Additionally, StructMG significantly improves strong and weak scaling efficiencies in most tests.