Since optimization on Riemannian manifolds relies on the chosen metric, it is appealing to know that how the performance of a Riemannian optimization method varies with different metrics and how to exquisitely construct a metric such that a method can be accelerated. To this end, we propose a general framework for optimization problems on product manifolds where the search space is endowed with a preconditioned metric, and we develop the Riemannian gradient descent and Riemannian conjugate gradient methods under this metric. Specifically, the metric is constructed by an operator that aims to approximate the diagonal blocks of the Riemannian Hessian of the cost function, which has a preconditioning effect. We explain the relationship between the proposed methods and the variable metric methods, and show that various existing methods, e.g., the Riemannian Gauss–Newton method, can be interpreted by the proposed framework with specific metrics. In addition, we tailor new preconditioned metrics and adapt the proposed Riemannian methods to the canonical correlation analysis and the truncated singular value decomposition problems, and we propose the Gauss–Newton method to solve the tensor ring completion problem. Numerical results among these applications verify that a delicate metric does accelerate the Riemannian optimization methods.