In the state of the art of modeling and simulation of wind turbines, verification and validation (V&V) is a somewhat underdeveloped field. The purpose of this paper is to spotlight the process of a completely integrated V&V procedure, as it is applied to a wind turbine blade. The novelty, besides illustrating the application of V&V to blade modeling, is to challenge the conventional separation between verification and validation activities. First, simple closed-form solutions for bending stress, torsional stress and mode shapes of a hollow cylinder are derived analytically to verify the ANSYS finite element software. Shell-281 elements are used to approximate these closed-form solutions and demonstrate that the software runs properly. The grid convergence index is used to quantify the degree of numerical uncertainty that results. Next, model development and verification activities are applied to the CX-100 blade designed at the Sandia National Laboratories. A three-dimensional model is developed based on the actual geometry of the CX-100 blade. For simplicity, the model assumes smeared cross sections with uniform, isotropic material properties. Solution verification is performed to quantify the numerical uncertainty due to mesh discretization of the finite element model. The mesh refinement study provides evidence that the model leads to numerical solutions located in the regime of asymptotic convergence. We depart from the conventional V&V paradigm by proposing that the level of mesh discretization should be based on an assessment of experimental variability. Instead of choosing the mesh size ‘in a vacuum’, it is selected such that the overall numerical uncertainty caused by truncation effects is similar to, or smaller than, the test-to-test variability. This rationale guarantees that predictions are sufficiently accurate relative to the level of uncertainty with which physical tests can be replicated. Part II of this work highlights the V&V steps implemented to quantify sensitivities of the model and further quantify the prediction uncertainty caused by our imperfect knowledge of the idealized material description. Copyright © 2012 John Wiley & Sons, Ltd.