A Generative Framework for Vegetation–Weather Interactions and Extreme Response Analysis

Understanding and predicting the relation between plant productivity and meteorological drivers is central to ecosystem and climate science. Existing approaches fall into two broad categories: process-based models and data-driven models. Process-based models can represent causal relationships and allow users to prescribe and perturb variables, but at global scales they are either computationally expensive or simplified to the point that key processes and ecosystem diversity are lost. Data-driven models (e.g., FluxCom) produce only mean responses and therefore miss internal variability of meteorology, vegetation state, and fluxes. Because these approaches impose a fixed split between inputs and outputs, one must decide in advance which variables can be conditioned on and which will be predicted, which constrains the effect of perturbations and limits experimentations. We address these complementary shortcomings by developing a probabilistic model of vegetation and weather state variables using generative diffusion models trained on FluxNet data. As a consequence, the model can sample plausible trajectories that reflect the full distribution. We demonstrate two key capabilities. First, the model functions as a data-driven emulator that can be conditioned on specified inputs, such as prescribed temperature, radiation, or soil moisture, while producing ensemble outputs that capture uncertainty and internal state variability. This enables users to explore vegetation responses similarly to typical mechanistic models, but at a fraction of the computational cost and with observational grounding. Second, we exploit the stochastic model to analyze vegetation responses to extreme weather events. Unlike approaches predicting the mean, our diffusion-based emulator reveals how extreme meteorological inputs alter the tails of the vegetation response distributions. By bridging the gap between mechanistic workflows and data-driven models, our diffusion model offers a practical path toward both improved scientific understanding of vegetation–weather interactions and an operational product for future analyses, risk assessment, and scenario exploration.