Increased frequency of stress events such as heat waves has been observed for the last decades. Based on the last IPCC report, they are expected to be more frequent, to last longer and to increase in intensity during the reproductive phase of economically important crops. Many recent studies pointed out induced memory effects of stressing events when plants are challenged several times with similar stresses throughout the crop season. These memory effects were shown to be potentially beneficial since the plants are 'primed' and thus more prepared to develop an earlier, more rapid, intense and/or sensitive response when the stress recurs [1]. Therefore, the new climatic patterns prompts to take into account stress memory into predictive crop modelling approaches so as to estimate the effects of repeated stresses and their consequences on crop yield, quality of harvested products. During the last decades, the use of crop models have been enlarged to climate change driven predictions [2]. While evidence for improving crop climate models and especially the temperature response functions in order to reduce uncertainty in yield simulations before any decision making in agriculture, no modelling studies have attempted to decipher and interpret simulation bias in the light of stress memory nor they focused on methodologies to take into account stress memory effects.