Beyond Better Data: Understanding the True Demand for Climate Predictions in Adaptation Practice

Climate services and co-production efforts aim to support climate adaptation by making forecasts and predictions more usable. In recent years, demand-driven approaches have become a key strategy. However, many initiatives still prioritize improving data over understanding real-world decision needs. Case studies often rely on unclear sampling, limited methods for exploring decision-making and a narrow focus on information provision. This limits our understanding of the true demand for better climate predictions, especially in relation to other, potentially more impactful, levers for adaptation.

Here, we apply a behavioral and social science perspective to evaluate the actual and potential role of improved climate predictions in supporting effective societal adaptation. To this end, we select two most climate-sensitive sectors in Germany—agriculture and forestry—as test beds to examine how predictions on the 10-year horizon (could) inform decision-making under uncertainty. Through four expert focus groups (N = 24), we interview systematically sampled participants with deep sectoral knowledge of climate impacts, adaptation options, and real-world decision contexts. We identify weather- and climate-sensitive decisions, elicit decision strategies and information needs, and evaluate the relative importance of improved predictions compared to other factors related to capabilities, opportunities, and motivation.

Our findings reveal that climate predictions are currently not used in practice. Instead, experts rely on observations, weather information, and regionalized projections. They report and work with decision strategies such as worst-case planning or diversification that do not depend on precise forecasts. Nevertheless, participants expressed interest in more informative predictions that go beyond means. Higher resolution was not always seen as essential and trade-offs considered possible. Importantly, demand was shaped not only by information needs, but also by cognitive, institutional, and motivational constraints. Understanding and articulating demand for climate information is thus a complex task—what users actually need, their true demand, often differs from what is assumed.

Our results suggest that better societal adaptation may mostly not require better predictions, but better integration of existing information into real-world decision-making. This includes prioritizing contextual relevance, simplifying outputs without oversimplifying meaning, and addressing the behavioral and institutional constraints that hinder information uptake. We show how research in this direction can look like and how this can be achieved methodologically.