Integrated assessment models (IAMs) are essential analytical tools in climate change science. There is wide recognition of the need of credible IAM scenarios for guidance on developing climate change mitigation and adaptation measures. This dissertation employs the Global Change Analysis Model (GCAM), a state-of-the-art IAM, in three studies that develop meaningful scenario analyses of climate change mitigation and impacts to address key gaps in the contemporary IAM research. The first study deals with the challenge of reconciling mitigation strategies consistent with the Paris Agreement climate goals with constraints on energy-water-land (EWL) resources. The study highlights the fact that mitigation strategies can have unintended repercussions for the EWL sectors, which can undermine their overall effectiveness. In Latin American countries used as case studies, increased water demands for crop and biomass irrigation and for electricity generation stand out as potential trade-offs resulting from climate mitigation policies. The second study demonstrates that scenarios that explore the consequences of climate change impacts on renewable energy for the electric power sector need to adopt a comprehensive modeling approach that accounts for climate change impacts in all renewables. Using such an approach, the findings from this study show that climate impacts on renewables can result in additional capital investment requirements in Latin America. Conversely, accounting for climate impacts only on hydropower – a primary focus of previous studies – can significantly underestimate investment estimates, particularly in scenarios with high intermittent renewable deployment. The last study demonstrates that GCAM projections of solar photovoltaics and wind onshore electricity generation can be largely affected by methodological uncertainties in the computation of global renewable energy potentials – used to produce resource cost-supply curves that are key input assumptions to IAMs. Consequently, the role of these renewables in the modeled long-term scenarios can be under- or overestimated with potential implications for decision-making on energy planning, climate change mitigation and on the adaptation efforts to climate impacts on these renewables. The three studies encompass questions that have received little or no attention by the IAM community, and contribute with relevant approaches and insights that offer improvements relative to prior analyses. Importantly, these results help to enhance the value of GCAM scenarios to decision-making and identify research opportunities that might help improve GCAM as well as other IAM projections.