Creating internally consistent climate scenarios remains a challenge in climate research. While Earth System Models (ESMs) offer detailed regional outcomes able to resolve and discover physical dynamics and drivers, their computational cost prevents them from covering the full range of emissions and land-use scenarios produced by Integrated Assessment Models (IAMs). As a result, the modelling chain from IAMs through Simple Climate Models (SCMs) to ESMs informing climate change assessments remains limited in its full deployment to a handful of scenarios, leaving most IAM scenarios lacking their corresponding regional climate information and limiting our ability to assess their implications for local impacts. This modelling chain also remains largely unidirectional: Climate outcomes are seldom fed back to inform the underlying scenarios whose development therefore may miss important potential feedback between physical climate and socioeconomic futures. Here, we introduce FASTMIP (Fast Assessment for Scenario Trajectories Multi-emulator Intercomparison Project), a coordinated effort to rapidly translate socioeconomic scenarios into spatial climate outcomes using emulators. In this FASTMIP pilot experiment, we apply three spatial emulators (MESMER, PRIME, and STITCHES) to a shared set of scenarios that includes both widely used CMIP6 pathways and scenarios without dedicated ESM simulations. The results show how spatial emulation could provide information on climate feedback to constrain scenario assumptions, extend scenario-to-climate coverage, support regionally differentiated assessments, and therefore reveal current limitations in the climate projection pipeline that limit a more comprehensive linkage and evaluation of scenario-to-climate outcomes. We show that for these 3 emulators there is consistency in the results, particularly for the center of the distribution. Addressing the limitations identified here at the scenario-to-climate interface could enable spatial emulators to systematically deliver rapid regional insights and support more consistent climate scenario development in future coordinated IAM-ESM modelling exercises.