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Emulators are an efficient method to generate complex model outputs, such as earth system projections, using physics-based reduced complexity models or algorithm-based approaches like artificial intelligence. Reduced complexity models played a central role in probabilistic assessment of global warming levels in the IPCC Sixth Assessment Report. Some of the key assessments presented in the report, including warming contributions from individual climate forcing agents and warming-level categorization for numerous climate mitigation scenarios, were based on large-scale parameter ensemble experiments with reduced complexity models. The global warming level is a commonly used indicator in climate, impact, and mitigation scenarios, and the parameter ensemble reflects findings based on modeling, observations, and process understanding. From this perspective, the emulator can be seen as a tool to interconnect and integrate information from various disciplines. Climate patterns such as spatial distribution and seasonal changes, which are necessary for impact assessment, can be handled by algorithm-based emulators. Impact model emulators have also been developed to easily assess sectoral and regional impacts for various climate scenarios. This session will focus on the role of such emulators and aims to share individual findings related to emulator methodology and applications broadly to deepen mutual understanding.