Terrestrial ecosystems constitute a major component of the global carbon sink and play a critical role in regulating the global carbon cycle. Although process-based models such as the Ecosystem Demography (ED) model have been developed to simulate these dynamics and widely adopted in research and applications, they remain computationally intensive and are not well suited for large-scale (e.g., global) projections at high spatial and temporal resolution, or under wide-range of future scenarios. AI-based emulators of process-based physical models have emerged as promising directions to accelerate the computation, with encouraging success in fields such as weather forecasting (e.g., Google's GraphCast and NVIDIA's FourCastNet). There are several challenges to develop emulators for ecosystem processes, including error accumulation over long-sequences, single-step initial conditions, and high-dimensional environment conditions. Existing works often rely on time-series patterns in look-back windows and are not well-suited for the problem with single-step initial conditions. Moreover, they often do not consider uncertainty, making it hard to know when the approximations are highly confident and when the results may need to be updated, e.g., by the process-based models. To address these limitations, we introduce EcoDiffusion, a conditional diffusion framework tailored for ecosystem dynamics emulation to effectively condition on large volumes environment variables and generate non-autoregressive forecasts at multiple scales with uncertainty-awareness. We carried out testing cases at locations distributed over the globe under different scenarios and EcoDiffusion demonstrated significant improvements over existing models.