<p>Environmental decision-makers increasingly confront non-stationary systems where predictive models fail, yet management actions remain urgent. Conventional approaches assume sufficient data and stability for forecasting, but these assumptions are often violated in data-limited contexts of the Global South. We present a diagnostic forecasting framework that acknowledges irreducible uncertainty and provides decision-support tools for adaptive governance. Rather than pursuing complex models that may produce misleading precision under irreducible uncertainty, our framework emphasizes diagnostic capacity: understanding system state, identifying stressors, decomposing uncertainty, and preparing for plausible futures. Applied to a 35-year wetland fisheries dataset from Bangladesh, model selection uncertainty contributed 40% of forecast variance, with prediction intervals exceeding historical variability. While trends in climate variables were significant, climate–ecology relationships became nonsignificant when controlling for shared temporal trends, indicating dominance of local anthropogenic stressors. The framework delivers three management-ready outputs: diagnostic monitoring, stressor-led intervention pathways, and scenario-based decision rules transferable across socioecological contexts worldwide.</p> Graphical abstract <p></p>