From Better Climate Data to Better Decisions Through True Demand

Climate services and co-production efforts aim to support adaptation by making climate information useful and usable. Yet many services and research projects prioritize improving data quality over understanding decision-making needs. This limits insights into what we define as true demand: the actionable need for information that would improve decisions, if conditions allowed. Here, we present a systematic sectoral analysis grounded in behavioral theory exploring whether, where, and how better climate predictions could improve decisions. We apply this approach to two climate-sensitive sectors in Germany: agriculture and forestry. Using expert focus groups (N = 24), we identify key adaptation decisions and analyze current decision strategies. Drawing on the COM-B model for behavior (B) change, we examine the capabilities (C), opportunities (O), and motivation (M) necessary to use climate predictions in practice. Our findings reveal that foresighted adaptation is often constrained by gaps in knowledge, lack of opportunity, or limited motivation. These constraints diminish the perceived importance of climate predictions on the subseasonal to decadal timescale or prevent their use, with decision-makers instead relying on past experience and heuristics such as worst-case planning. Nevertheless, experts expressed interest in improved predictions---particularly for variability and extremes across intra-annual to multi-year timescales---that could inform decisions. True demand may remain masked, blocked, or unexpressed if decision-makers lack the capabilities, opportunities, or motivation to recognize, act on, or articulate it. Our approach provides a systematic way to assess where and how predictions could improve decisions, shifting the focus from better data to better decisions.