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Abstract Climate models disagree on the direction of precipitation change over about half of the Earth. Current characterizations of expected change use the ensemble mean, which systematically underestimates the magnitude and overestimates the time of emergence (ToE) of precipitation change in regions of high uncertainty. We develop a new approach to estimate both ToE and the potential to update uncertainty in precipitation over time with new observations. Further, we develop two new metrics that increase the usefulness of ToE for adaptation planning. The time of confidence estimates when projections of precipitation emergence will have high confidence. Second, the advance warning time (AWT) indicates how long policymakers will have to prepare for a new precipitation regime after they know change is likely to occur. Our approach uses individual model projections that show change before averaging across models to calculate ToE. It then applies a Bayesian method to constrain uncertainty from climate model ensembles using a perfect model approach. Results demonstrate the potential for widespread and decades‐earlier precipitation emergence, with potential for end‐of‐century emergence to occur across 99% of the Earth compared to 60% in previous estimates. Our method reduces uncertainty in the direction of change across 8% of the globe. We find positive estimates of AWT across most of the Earth; however, in 34% of regions there is potential for no advanced warning before new precipitation regimes emerge. These estimates can guide adaptation planning, reducing the risk that policymakers are unprepared for precipitation changes that occur earlier than expected. 

Climate oscillations ranging from years to decades drive precipitation variability in many river basins globally. As a result, many regions will require new water infrastructure investments to maintain reliable water supply. However, current adaptation approaches focus on long-term trends, preparing for average climate conditions at mid- or end-of-century. The impact of climate oscillations, which bring prolonged and variable but temporary dry periods, on water supply augmentation needs is unknown. Current approaches for theory development in nature-society systems are limited in their ability to realistically capture the impacts of climate oscillations on water supply. Here, we develop an approach to build middle-range theory on how common climate oscillations affect low-cost, reliable water supply augmentation strategies. We extract contrasting climate oscillation patterns across sub-Saharan Africa and study their impacts on a generic water supply system. Our approach integrates climate model projections, nonstationary signal processing, stochastic weather generation, and reinforcement learning–based advances in stochastic dynamic control. We find that longer climate oscillations often require greater water supply augmentation capacity but benefit more from dynamic approaches. Therefore, in settings with the adaptive capacity to revisit planning decisions frequently, longer climate oscillations do not require greater capacity. By building theory on the relationship between climate oscillations and least-cost reliable water supply augmentation, our findings can help planners target scarce resources and guide water technology and policy innovation. This approach can be used to support climate adaptation planning across large spatial scales in sectors impacted by climate variability. 

Abstract Uncertainty arising from climate change poses a central challenge to the long‐term performance of many engineered water systems. Water supply infrastructure projects can leverage different types of flexibility, in planning, design, or operations, to adapt infrastructure systems in response to climate change over time. Both flexible planning and design enable future capacity expansion if‐and‐when needed, with flexible design proactively incorporating physical design changes that enable retrofits. All three forms of flexibility have not previously been analyzed together to explicitly assess their relative value in mitigating cost and water supply reliability risk. In this paper, we propose a new framework to evaluate combinations of flexible planning, design, and operations. We develop a nested stochastic dynamic optimization approach that jointly optimizes dam development and operating policies under dynamic climate uncertainty. We demonstrate this approach on a reservoir project near Mombasa, Kenya. Our results find that flexible operations have the greatest potential to reduce costs. Flexible design and flexible planning can amplify the value of flexible operations under higher discounting scenarios and when initial infrastructure capacities are undersized. This approach provides insight on the climate change and techno‐economic conditions under which flexible planning, design, and operations can be best leveraged individually or in combination to reduce climate change uncertainty risks in water supply infrastructure projects.