Abstract: Climate projections through process-based statistical crop models are important in studying the impacts of climate change on agricultural production. However, extensive assessments have generally relied on single climate with single crop models which have shown large discrepancies in predicted crop yields and estimations uncertainty hardly assessed. The proper understanding of uncertainties associated with such models is essential for effective use of projected results in devising adaptation strategies. Assessing crop yield response to future climate conditions based on an ensemble of possible outcomes from multiple climate projections and crop models could be more reliable than using a single model outcome. To estimate uncertainties associated with the study of the impacts of climate change on crop yield, we used 8 climate projections by GCMs under RCP4.5 in the CMIP5 (which represented the uncertainties in the projected climate change) and a statistical process-based crop model (which represented the uncertainties in the different structures or different formulations of physiological processes of crop models). Historical data of crop and meteorological data during 1981-2009 from agro-meteorological stations of China Meteorological Administration in Hailun, Changling and Benxi in Northeast China were used to establish and evaluate statistical and process-based APSIM (Agricultural Production Systems sIMulator) models, respectively. Then the two crop models were linked with 8 climate projections to evaluate the impact of climate change on maize yield during 2010-2039 and 2040-2069, using 1976-2005 as the baseline period. In total, 2 crop models under 8 climate projections for a period of 30 years (a total of 480 simulations) were generated for both the baseline and two future climate periods. The results showed that APSIM model well simulated the growth and yield of maize. The root mean square error (RMSE) for the growth progress (flowering and maturity) simulation was 3-4 days and that for the yield simulation was 0.6-0.8 t·hm^-2. The established statistical model suggested that temperature during emergence (mid May) had a positive effect on maize yield. However, the increase of temperature and rainfall, and lack of solar radiation during flowering and grain-filling periods (mid July to early September) had negative impact on increase of maize yield. Compared with 1976-2005, the resulting probability distributions indicated that due to climate change, maize yield in 2010-2039 decreased on average by 3.8% (Hailun)-7.4% (Benxi), at a probability of 64% (Changling)-73% (Benxi). During 2040-2069, maize yield increased by 6.4% (Hailun)-10.5% (Benxi), at a probability of 74% (Hailun)-83% (Benxi). The simulated yield decrease by the APSIM model was 6.6% (Hailun)-8.9% (Benxi) during 2010-2039 and 9.7% (Hailun)-13.7% (Benxi) during 2040-2069. These were higher relative to those simulated by the statistical model, which were 0.9% (Hailun)-6.0% (Benxi) during 2010-2039 and then 2.0% (Changling)-7.3% (Benxi) during 2040-2069.