This paper develops a novel methodology to estimate the degree of spatial basis risk for an arbitrary rainfall index insurance instrument. It relies on a widelyused stochastic rainfall generator, extendedto accommodate nontraditional dependence patterns—in particular spatial upper-tail dependence in rainfall—through a copula function. The methodology is applied to a recentlylaunched index product insuring against excess rainfall in Uruguay. The model is first calibrated using historical daily rainfall data from the national network of weather stations, complemented with a unique,high-resolution dataset from a dense network of 34 automatic weather stations around the study area. The degree of downside spatial basis risk is then estimated by Monte Carlo simulations and the results are linked to both a theoretical model of the demand for index insurance and to farmers’ perceptions about the product.