The agriculture sector is facing considerable challenges in ensuring food security under projected changes in climate and pressures to reduce its environmental impacts, among others. With changes in growing season and local growing conditions already being observed, adaptation is a key factor in aiming towards climate-smart, sustainable agriculture. Process-based crop models offer a tool for understanding complex interactions associated with crop, environment and management actions, and quantifying their impacts on various outputs. In the face of uncertainties associated with impact estimates, risk assessment has become an essential part of adaptation planning. This study explored the use of a “scenario-neutral” approach for informing risk assessments in the context of crop production. Its main motivation was to examine novel insights offered by the approach for characterising uncertainties associated with modelled impacts compared to conventional scenario-based approaches, where impact estimates are tied to a given scenario. The approach utilises impact response surfaces (IRSs) to depict simulated period-mean sensitivities of cereal yields to systematic changes in baseline (1981–2010) temperature and precipitation. The analysis focused on sites in Finland, Germany and Spain, across a transect of contrasting environmental zones that hence facilitated an examination of the effect of site-specific growing conditions on the impacts of projected changes on cereal yields. The research encompassed a multi-model IRS study involving 26 crop models for wheat as well as an IRS study employing a single model for barley. In addition to analysing median responses of the model ensemble across the transect, approaches were developed for classifying and interpreting individual model responses. By combining IRSs with projections of climate interpreted probabilistically, likelihood of crop yield shortfall was estimated and its evolution throughout the 21st century visualised. This was estimated with a single crop model WOFOST for spring barley in Finland. Effects of adaptation on yield were considered through adapted sowing and cultivar choice. Evolution of future atmospheric carbon dioxide concentration [CO2] defined by representative concentration pathways also used for climate projections (RCP4.5 and RCP8.5) was also considered when estimating likelihoods. With the multi-model ensemble study of wheat yield sensitivities [CO2] was fixed at 360 ppm. Simulated cereal yields were found to decline with warming and drying and increase with higher precipitation. The yield response in Finland was dominated by temperature. Precipitation change dominated the response of spring wheat in Spain, while the response was more mixed in Germany. The multi-model ensemble median response offered a consensus view of impact sensitivities, with individual model behaviour occasionally departing markedly from the average. IRS patterns across the multi-model ensemble showed greater similarity in the pattern of modelled yield responses for Germany in comparison to Finland and Spain. Similarity in patterns was also associated with models of related genealogy. With respect to the effectiveness of tested adaptation options, results suggest that combining cultivars with short pre- and long post-anthesis phases with earlier sowing, offers most promise for obtaining the largest yield gains and smallest likelihoods of yield shortfall under future scenarios. Higher levels of [CO2] generally compensate for yield losses with warming, with the effect emphasised with the biggest increases in temperature. IRSs offer a valid alternative to conventional scenario-based approaches with many advantages for presenting and analysing results. IRSs can assist in model testing, comparison of results across models, studies and sectors and examination of various statistical characteristics of the response, greatly facilitated by the possibility to visually depict impact sensitivities in consistent ways. Use of multi-model ensembles with respect to both climate projections and crop impacts increases the robustness of results and provides information on the uncertainties around the yield estimates. The approach for estimating and visualising impact likelihoods provides improved understanding and transparency of concepts behind the likelihood estimates.