Although China has the largest trading volume of commodity futures, limited studies have been devoted to the term structure of Chinese commodity futures. This paper takes the tools in functional data analysis to understand the term structure of commodity futures and forecast its dynamic changes at both short and long horizons. Functional ANOVA has been applied to examine the calendar e_ect of term structure in level and _nd the seasonality in the commodity futures of coking coal and polypropylene. We use an h-step functional autoregressive (1) model to forecast the dynamic change of term structure. Comparing with native predictor, in-sample and out-of-sample forecasting performance indicate that additional forecasting power is gained by using the functional autoregressive structure. Although the dynamic change at short horizons is not predictable, the forecasts appear much accurate at long horizons due to the stronger temporal dependence. The predictive factor method has a better in-sample _tting, but it cannot outperform the estimated kernel method for out-of-sample testing, except for 1-quarter-ahead forecasting.