A methodology to quantify uncertainty in wildfire forecast using coupled fire-atmosphere computational models is presented. In these models, an atmospheric solver is coupled with a fire-spread module. In order to maintain a low computational cost, the atmospheric simulation is limited to a coarse numerical resolution, which increases the uncertainty in the wildfire spread prediction. Generalised polynomial chaos is proposed to quantify this uncertainty and obtain a response function for the forecast error in terms of the atmospheric resolution and the forecast horizon. The response is obtained from a set of simulations of a grassland fire with an in-house coupled fire-atmosphere model at varying degrees of resolution. Global sensitivity analysis of the response shows that the resolution is the primary parameter affecting the error. However, due to the strongly coupled fire-atmosphere dynamics in the initial fire development, the forecast time horizon locally becomes the dominant variable. A parametric study on the effect of the fire spreading regime suggests that the forecast uncertainty is large in plume-dominated spreading conditions: coarse resolution forecasts accumulate a large error because they cannot capture the intense small-scale vortical motion at the fire front.