The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013-2018 from Assessment, Inventory, and Monitoring (AIM) instead of using the 2016 “base” map as an intermediary. This removes one level of model error and allows the direct association of high-resolution derived training data to the corresponding year of Landsat imagery. We have incorporated all available (as of 10/1/22) Bureau of Land Management (BLM), Assessment, Inventory, and Monitoring (AIM), and Landscape Monitoring Framework (LMF) observations. LANDFIRE public reference database training observations spanning 1985-2015 have been added. Neural network models with Keras tuner optimization have replaced Cubist models as our classifier. We have added a tree canopy cover component. Our study area has expanded to include all of California, Oregon, and Washington; in prior generations landscapes to the west of the Cascades were excluded. Additional spectral indices have been added as predictor variables, tasseled cap wetness, brightness, and greenness. Location information (i.e., latitude and longitude/ x and y coordinates) and elevation above sea level have been added as predictor variables. CCDC-Synthetic Landsat images were obtained for 6 monthly periods for each region and were added as predictors. These data augment the phenologic detail of the 2 seasonal Landsat composites. Post-processing has been improved with updated fire recovery equations stratified by ecosystem resistance and resilience (R and R) classes (Maestas and Campbell 2016) to stratify recovery rates. Ecosystem R and R maps are only available for the sagebrush biome. We intersected classes with 1985-2020 average water year precipitation to identify precipitation thresholds corresponding to R and R classes. Outside of the sagebrush biome, precipitation was used to produce R and R equivalent (low, medium, high). Due to the fast recovery following fire in California chapparal (e.g., Keeley and Keeley 1981, Storey et al. 2016), we used EPA level 3 ecoregions to define a 4th R and R zone. Recovery rates are based on (Arkle et al (in press)) who evaluated the recovery of plant functional groups in 1278 post-fire rehab plots by time since disturbance stratified by ecosystem resistance and resilience. We have expanded this analysis by evaluated postfire-recovery in all AIM and LMF data across the West to establish maximum sage, shrub, and tree cover by time-since fire. Recovery limits in California follow (Keeley and Keeley 1981 and Storey et al. 2016). Second, post-processing has been enhanced through a revised noise detection model. For each pixel, we fit a third order polynomial model for each component cover time-series. Observations with a z-score more than 2 standard deviations from the mean are removed, and a new third order polynomial model (i.e., cleaned fit) is fit to observations within this threshold. Finally, looking again at all observations, those observations with a z-score more than 2 standard deviations from the mean of the cleaned fit are replaced with the mean of the prior and subsequent year component cover values. Processing efficiency has been increased using open-source software and USGS High-Performance Computing (HPC) resources. The mapping area included eight regions which were subsequently mosaicked for all nine components. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. Component products can be downloaded https://www.mrlc.gov/data.