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Abstract Accurate assessment of changes in water availability with changing climate is vital for effective mitigation and adaptation. In this research, we employ a parsimonious Budyko curve method to evaluate changes in water availability under low‐ (SSP126) and high‐emission (SSP585) scenarios for 331 river basins in the contiguous United States. We also assess the relative role of changes in precipitation (∆P) and potential evapotranspiration (∆PET) with changing climate on the increase in water availability vulnerability. Results highlight that around 43% (28%) of basins are projected to experience increased vulnerability to changing climate in high‐emission (low‐emission) scenarios. Sub‐humid basins are most often impacted, while arid and semi‐arid basins exhibit lower sensitivity to changes. Intriguingly, ∆PET emerges as the dominant control on vulnerability, surpassing ∆P, particularly under SSP585 scenario. The analysis prompts water managers to focus on long‐term mitigation planning and scientists to further constraint climate and water budget forecasts in affected basins. 

The Sava River Basin (SRB) includes six countries (Slovenia, Croatia, Bosnia and Herzegovina, Serbia, Albania, and Montenegro), with the Sava River (SR) being a major tributary of the Danube River. The SR originates in the mountains (European Alps) of Slovenia and, because of a recent Slovenian government initiative to increase clean, sustainable energy, multiple hydropower facilities have been constructed within the past ~20 years. Given the importance of this river system for varying demands, including hydropower (energy production), information about past (paleo) dry (drought) and wet (pluvial) periods would provide important information to water managers and planners. Recent research applying traditional regression techniques and methods developed skillful reconstructions of seasonal (April–May–June–July–August–September or AMJJAS) streamflow using tree-ring-based proxies. The current research intends to expand upon these recent research efforts and investigate developing reconstructions of seasonal (AMJJAS) precipitation applying novel Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques. When comparing the reconstructed AMJJAS precipitation datasets, the AI/ML/DL techniques statistically outperformed traditional regression techniques. When comparing the SRB AMJJAS precipitation reconstruction developed in this research to the SRB AMJJAS streamflow reconstruction developed in previous research, the temporal variability of the two reconstructions compared favorably. However, pluvial magnitudes of extreme periods differed, while drought magnitudes of extreme periods were similar, confirming drought is likely better captured in tree-ring-based proxy reconstructions of hydrologic variables.