Land models, which have been developed by the modeling community in the past few decades to predict fu-ture states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosys-tem responses and feedback to climate change. Benchmark-ing is an emerging procedure to measure performance of models against a set of defined standards. This paper pro-poses a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major chal-lenges at this infant stage of benchmark analysis. The frame-work includes (1) targeted aspects of model performance to be evaluated, (2) a set of benchmarks as defined refer-ences to test model performance, (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4) model improve-ment. Land models are required to simulate exchange of wa-ter, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochem-ical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of Published by Copernicus Publications on behalf of the European Geosciences Union. 3858 Y. Q. Luo et al.: A framework for benchmarking land models benchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mis-matches between models and benchmarks. The metrics may include (1) a priori thresholds of acceptable model perfor-mance and (2) a scoring system to combine data–model mis-matches for various processes at different temporal and spa-tial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on de-velopment of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fun-damental properties of land models to improve their predic-tion performance skills.