Quantifying the historical range and variability of landscape composition and structure using simulation modeling is becoming an important means of assessing current landscape condition and prioritizing landscapes for ecosystem restoration. However, most simulated time series are generated using static climate conditions which fail to account for the predicted major changes in future climate. This paper presents a simulation study that generates reference landscape compositions for all combinations of three climate scenarios (warm-wet, hot-dry, and current) and three fire regime scenarios (half historical, historical, and double historical fire frequencies) to determine if future climate change has an effect on landscape dynamics. We applied the spatially explicit, state-and-transition, landscape fire succession model LANDSUM to two large landscapes in west-central Montana, USA. LANDSUM was parameterized and initialized using spatial data generated from the LANDFIRE prototype project. Biophysical settings, critical spatial inputs to LANDSUM, were empirically modeled across the landscape using environmental gradients created from historical and modeled future climate daily weather data summaries. Successional pathways and disturbance probabilities were assigned to these biophysical settings based on existing field data and extensive literature reviews. To assess the impact of changes in climate and fire regime, we compared simulated area burned and landscape composition over time among the different simulation scenario combinations using response variables of Sorenson's index (a global measure of similarity) and area occupied by the dominant vegetation class (simple indicator of change in landscape composition). Results show that simulated time series using future predicted climate scenarios are significantly different from the simulated historical time series and any changes in the fire regime tend to create more dissimilar and more variable simulated time series. Our study results indicate that historical time series should be used in conjunction with simulated future time series as references for managing landscapes.