**4. Discussion and Conclusions**

This study investigated potential changes in future precipitation, temperature, and drought (as represented by SPEI) across 10 hydrologic regions defined by the California Department of Water Resources. The latest climate model projections on these variables through 2099 representing the state of the current climate science were applied for this purpose. Changes were explored in terms of differences from a historical baseline as well as the changing trend.

Results indicate that warming is expected across all regions in all temperature projections, particularly in late-century. There is no such consensus in precipitation, with projections ranging mostly from −25% to +50% different from the historical baseline. There is no statistically significant increasing or decreasing trend in historical precipitation as well as in the majority of the projections. However, on average, precipitation is expected to increase slightly for most regions. It should be noted that this finding is not completely in line with a previous study that indicates decreases in future California precipitation [81]. The major difference stems from the fact that different sets of data are applied in the two studies. Specifically, the current study focused on precipitation and temperature projections from 10 GCMs models (versus 42 models used by the previous study) that are deemed most appropriate for water resources planning studies in California. Compared to wet regions, dry regions are projected to have more severe drought conditions represented by SPEI. Those findings are generally consistent with what have been reported in previous studies [21,26,28,76]. A new finding of this study is that the coolest region, North Lahontan, tends to have the highest increases in both minimum and maximum temperature and a significant amount of increase in wet season precipitation, indicative of naturally increasing flood risk in this region. In another new finding, the warming in

summer and fall (when water demand is typically high and precipitation is limited) is expected to be more significant than the warming in winter and spring

In general, the findings of this study are meaningful from both scientific and practical perspectives. From a scientific point of view, these findings provide useful information that can be utilized to improve the current flood and water supply forecasting models. For instance, the coolest region, North Lahontan, is expecting the most significant warming as well as increases in wet season precipitation. This region is largely impacted by snow because of its high elevation. These expected changes will most likely intensify regional rainfall (more precipitation comes as rainfall as warming elevates the snowline) and spring snowmelt, increasing flood risks in the future. This region needs to be closely monitored in the future, particularly near and above the current snowline. The current flood forecasting model uses a parameter to cap the maximum possible snowmelt rate [82]. To reflect the expected warming, this parameter needs to be increased accordingly to better model snowmelt. Taking one step further, the snow accumulation and snowmelt processes based on which the current forecasting model is developed are derived under the stationary assumption. In a non-stationary environment, these processes need to be revisited and updated accordingly as relevant new observations become available. Additionally, the current snowmelt model is temperature-index based. Snowmelt is a thermodynamic process driven more by radiation than temperature. Development and implementation of radiation-driven snowmelt model in operations are ongoing and will be reported in our future work.

From a practical standpoint, these findings can help inform water managers in making adaptive management plans. For instance, vulnerability assessment is typically the first step in developing any mitigation and adaptation strategies [83]. Corresponding adaptation strategies such as supply diversification or increased volume management capacity should be tailored for the characteristics of the regions and their particular impacts to a changing climate. All in all, this study has the potential to help decision-makers move from a reactive position of responding to hydroclimatic events as they happen to a pro-active position with region-specific strategies for improved water resources management in the future. These strategies facilitate improving the resilience of California's physical water framework and the preparedness of its institutional framework via investments (e.g., where, when, on what, and how much) in advance.

Despite its scientific and practical significance in guiding long-term strategical water resources planning, the study addressed temperature and precipitation changes at annual and seasonal scales at the hydrologic region scale. For time-sensitive and localized activities including emergency response and management, those changes at a finer temporal and spatial scale at which extreme events occur need to be explored. Extreme climatic indices (e.g., daily maximum precipitation, heat wave, etc.) with daily resolution at the watershed scale have been extracted from the 20 climate projections applied in this study. They will be analyzed and presented in a follow-up study. Furthermore, as opposed to precipitation and temperature, streamflow runoff is normally the variable directly used to inform real-time decision making (e.g., determination of reservoir release schedule). Those climate projections have been used as input to drive a distributed hydrologic model, the Variable Infiltration Capability model, to produce daily inflow projections through 2099 for major water supply reservoirs in California. Those flow data will be analyzed in terms of volume, variability, and frequency and reported in a companion study.

**Acknowledgments:** The authors would like to thank four anonymous reviewers for their valuable comments that largely helped improve the quality of this study. The authors would also like to thank their colleague Mahesh Gatuam and Jianzhong Wang for discussions on previous studies leading to the current work. Technical editing from Charlie Olivares is acknowledged. The authors also want to thank John Andrew, Prabhjot (Nicky) Sandhu, and Jamie Anderson for their management support on the study. Any findings, opinions, and conclusions expressed in this paper are solely the authors' and do not reflect the views or opinions of their employer.

**Author Contributions:** The study was conceived by the authors together. M.H. conducted the study and wrote the paper. A.S., E.L. and M.A. provided critical discussions.

**Conflicts of Interest:** The authors declare no conflict of interest.
