Almost two-thirds of emerging market and developing economies rely heavily on resource sectors for economic activity, fiscal and export revenues. In these economies, economic planning requires sound baseline projections for the global prices of the commodities they rely on and a sense of the risks around such baseline projections. This paper presents a model suite to prepare well-founded forecasts for the global prices for oil and six industrial metals (aluminum, copper, lead, nickel, tin, and zinc). The model suite adapts six approaches used in the literature and tests their forecast performance. Broadly speaking, futures prices or bivariate correlations performed well at short horizons, and consensus forecasts and a large-scale macroeconometric model performed well at long horizons. The strength of Bayesian vector autoregression models lies in generating forecast scenarios. The sizable forecast error bands generated by the model suite highlight the need for policy makers to engage in careful contingency planning for higher or lower prices.