ESM emulation methods generally attempt to preserve the complex statistical characteristics of a particular ESM’s outputs for multiple variables and at time scales (often daily or monthly) relevant to impact models. Many existing ESM emulation methods, such as MESMER (Beusch et al., 2020; Nath et al., 2022; Quilcaille et al., 2022), rely on ‘bottom up’ methods, inferring from the ESM outputs available for training the details of some statistical process (or, more recently, a machine learning algorithm) able to generate new realizations with the same spatiotemporal behavior of the original ESM outputs, using as input in the generative phase only large scale information, like global average temperature (GSAT), that can be generated by a reduced complexity model, such as Hector, MAGICC, or FAIR (Hartin et al., 2015; Meinshausen et al., 2011; Smith et al., 2018). The STITCHES approach instead takes a top-down approach inspired by the warming-level style of analyses used by past Intergovernmental Panel on Climate Change reports (Arias et al., 2021; Core Writing Team & (eds.), 2023; V. Masson-Delmotte et al., 2018; VP Masson-Delmotte et al., 2021). Specifically, STITCHES takes existing ESM output and intelligently recombines time windows of these gridded, multivariate outputs into new instances of transient, 21st century trajectories by stitching them together on the basis of a target GSAT trajectory. The latter can represent an existing scenario (i.e., one that the ESM has run) or a new one that a simple model can produce, as long as the latter is intermediate to existing ones in forcing levels/GSAT. We encourage users to see the flowchart included in the STITCHES quickstart notebook and website, as well as in Tebaldi et al. (2022), for a visual example of this process. Tebaldi et al. (2022) of course contains the full details as well as more illustrative figures.