As more of the western US burns in large wildfires it is critical to managers and scientists to understand how these landscapes recovery post-fire. Tree regeneration in high severity burned landscapes determines if and how these landscapes become forested again, while changes in fuels structure influences how these landscapes may burn again. In this study we compiled two large datasets to understand region-wide patterns and drivers of tree regeneration and surface fuel accumulation post-fire. Our results demonstrated that natural tree regeneration in the Rocky Mountains is declining with increasingly hotter and drier climatic conditions and that close distance to living trees were critical for tree establishment. Additionally, surface fuel accumulation following wildfires remained low in the first few years, but peak 9-14 years post-fire, with repeated fires mitigating this accumulation of surface fuel biomass. After completing analyses, we conducted collaborative workshops to engage managers on science and work with managers to create decision support tools for post-fire landscapes. As a result of multiple workshops with managers, we co-produced a decision tree tool that managers are currently using to apply our science from this project. That decision tree tool was published as part of an invited review of post-fire tree regeneration studies recently conducted across the entire western US. The west-wide results were similar to what we found in our study region. The objectives of this project were completed on time (with a one-year extension) and within budget, resulting in 5 publications, four focus groups, four workshops, 11 presentations to managers, the public and scientist, as well as expansive popular press interest. This project involved six young career scientists, who recently completed their PhDs and trained multiple undergraduates in field data collection methods. This project worked collaboratively with other on-going Joint Fire Science Program projects, as well as built upon the work previously funded by JFSP, NSF, and other funding agencies to create these large datasets for meta-analyses.