Forecast verification evaluates the quality of the forecasts made by a model, using a variety of forecast scores developed for binary classes, multiple classes, continuous variables and probabilistic forecasts. Skill scores estimate a model’s skill relative to a reference model or benchmark. Problems such as spurious skills and extrapolation with new data are discussed. Model bias in the output predicted by numerical models is alleviated by post-processing methods, while output from numerical models with low spatial resolution is enhanced by downscaling methods, especially in climate change studies.