Adaptation practitioners and policy-makers seeking to act proactively in the face of climate change need decision-relevant climate information. However, climate modelling efforts typically provide information on metrics of climatological or meteorological relevance, such as averages or extremes in temperature and precipitation, whereas resource managers often need more specific climatic metrics that provide actionable information (e.g. start date of the rainy season for irrigation planning). The identification of such decision-relevant metrics, is hence a critical first step towards developing usable climate information. Yet, few studies have systematically identified such metrics for sectoral adaptations, or the processes by which these metrics can be identified. Further, these metrics can be difficult to identify, because decision-makers may not always know the types of metrics that could be most useful, and scientists may not always know whether they can provide decision-relevant science with reasonable skill. ‘Co-production’, or iterative and continual engagement between scientists and decision-makers, can bridge the gap between user needs and scientific priorities, and help identify these actionable metrics.

This research is a case of co-production (Project Hyperion) where climate scientists and water managers (eventually) crossed the boundaries of both institutional mandate and epistemology, to co-produce decision-relevant metrics for water management. We examined how the metrics evolved iteratively through a year-long engagement effort, and found that engagement strategies that target both direct and indirect knowledge elicitation were effective approaches for translating managers’ needs into quantitative metrics. Additionally, the domain expertise of the boundary spanner, an under-appreciated phenomenon in the co-production literature, proved to be crucial for effective trans-boundary translation that enabled shared understanding across professional communities to emerge. The list of co-produced decision-relevant metrics developed from the project, serves as a significant outcome for both climate scientists and water managers. Such, collaborative metric identification efforts can push climate science and models in new and more use-inspired directions.