How the pattern of the Earth's surface warming will change under global warming represents a fundamental question for our understanding of the climate system with implications for regional projections. Despite the importance of this problem there have been few analyses of nonlinear local temperature change as a function of global warming. Individual climate models project nonlinearities, but drivers of nonlinear local change are poorly understood. Here, I present a framework for the identification and quantification of local nonlinearities using a time-slice analysis of a multi-model ensemble. Accelerated local warming is more likely over land than ocean per unit global warming. By examining changes across the model ensemble, I show that models that exhibit summertime drying over mid-latitude land regions, such as in central Europe, tend to also project locally accelerated warming relative to global warming, and vice versa. A case study illustrating some uses of this framework for nonlinearity identification and analysis is presented for north-eastern Australia. In this region, model nonlinear warming in summertime is strongly connected to changes in precipitation, incoming shortwave radiation, and evaporative fraction. In north-eastern Australia, model nonlinearity is also connected to projections for El Nino. Uncertainty in nonlinear local warming patterns contributes to the spread in regional climate projections, so attempts to constrain projections are explored. This study provides a framework for the identification of local temperature nonlinearities as a function of global warming and analysis of associated drivers under prescribed global warming levels.