As atmospheric CO 2 increases, ecosystem carbon sequestration will largely depend on how global changes in climate will alter the balance between net primary production and decomposition. The response of primary production to climatic change has been examined using well-validated mechanistic models, but the same is not true for decomposition, a primary source of atmospheric CO 2 . We used the Long-term Intersite Decomposition Experiment Team (LIDET) dataset and model-selection techniques to choose and parameterize a model that describes global patterns of litter decomposition. Mass loss was best represented by a three-pool negative exponential model, with a rapidly decomposing labile pool, an intermediate pool representing cellulose, and a recalcitrant pool. The initial litter lignin/nitrogen ratio defined the size of labile and intermediate pools. Lignin content determined the size of the recalcitrant pool. The decomposition rate of all pools was modified by climate, but the intermediate pool's decomposition rate was also controlled by relative amounts of litter cellulose and lignin (indicative of ligninencrusted cellulose). The effect of climate on decomposition was best represented by a composite variable that multiplied a water-stress function by the Lloyd and Taylor variable Q 10 temperature function. Although our model explained nearly 70% of the variation in LIDET data, we observed systematic deviations from model predictions. Below-and aboveground material decomposed at notably different rates, depending on the decomposition stage. Decomposition in certain ecosystem-specific environmental conditions was not well represented by our model; this included roots in very wet and cold soils, and aboveground litter in N-rich and arid sites. Despite these limitations, our model may still be extremely useful for global modeling efforts, because it accurately (R 2 5 0.6804) described general patterns of long-term global decomposition for a wide array of litter types, using relatively minimal climatic and litter quality data.