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The definition of food security provided by the Food and Agriculture Organization of the United Nations (FAO) includes the quality of agricultural products as a principal pillar, intended as the production of nutritious food to allow people to meet dietary needs and food preferences for an active and healthy life. In a world that is undergoing major physical, social, and economic transitions, the achievement of global food security is undermined by the projected increase of human population to 9 billion people by 2050. Nowadays, even if the current total food production would be capable to provide humankind with enough calories, the latest FAO statistics estimate that hundreds of millions of people live in hunger or lack a suitable supply of food. This is why the world governments are acting to meet the need of higher quality diets as a main objective. The challenge to improve the quality and the nutritional value of crop productions is also threatened by the climate change issue, with agriculture representing the most vulnerable economic sector due to the deep influence of weather conditions on the performances of cropping system. The only viable solution to gain information on the future trends of the qualitative aspect of crop production and to provide farmers and stakeholders in agriculture with effective adaptation strategies is the use of process based simulation models, which are capable to reproduce the responses of biophysical systems to changing boundary conditions. This doctorate gives answers to these research questions, by developing a reference methodological framework to assess the quality of rice (Oryza sativa L.) ?the first staple food crop in the world ? in current and future climatic conditions. The first chapter presents a software library of models to simulate the dynamics of the main aspects of rice grain quality as a function of agro-meteorological conditions. This research product is released as a framework independent component, fostering extension with new models and reuse by third parties intended as collaborations between research entities. In the second chapter the performances of the rice quality models in reproducing observed field data of milling quality and functional properties of grains are tested in a multi-site and multi-year evaluation, prior to be used to assess climate change impacts. The third chapter deals with the development of a forecasting system targeting the simulation of qualitative and quantitative rice productions in Northern Italy, the main European producing area. This pilot study is realized by coupling the WARM rice model with rice quality models, taking the head rice yield, i.e., the percentage of entire grains as a case study. The fourth chapter presents the complete workflow to assess the climate change impacts on crop productivity in the Lombardy plain via the application of process based models at a fine spatial resolution. An exploratory analysis of the impacts of climate change on giant reed crop is performed to illustrate the potentialities of the methodology. This work led the basis to the last chapter, where a comprehensive evaluation of the impacts of climate change on rice milling quality and technological suitability is performed in Europe. The main sources of uncertainties in climate change projections were taken into account, i.e., General Circulation Models and emission scenarios, to give an ensemble of future weather scenarios as input data to the models. The implementation of remote sensing to detect rice sowing dates and the assimilation of local farmers management led to a tight adherence between simulated and real system. The main perspective of this work is the application of the methodological framework developed here in top producing rice countries, in order to allow moving a step forward the mere focus on the quantitative trends of crop production in a changing climate.