**1. Introduction**

Understanding hydroclimatic changes and trends is of important scientific and practical significance for water resources management [1,2]. In particular, this understanding helps: (1) characterize the behavior of hydroclimatic variables (e.g., precipitation and temperature) as well as extreme events (e.g., droughts); (2) inform the development and enhancement of predictive tools to forecast future occurrence of these events; and (3) develop mitigation and adaptation plans to minimize the adverse impacts of unavoidable changes. This is particularly critical in arid and semi-arid areas including the State of California.

As the home to more than 37 million people [3] and a top-ten economy in the world, California's growth has been largely dependent on its ability to manage limited water resources [4]. In California, most of the precipitation falls in the northern half of the state, while the majority of the demand comes from the southern half where most of the population and farmlands are located. In addition, available water for supply in the state mostly comes during the wet season (November to April) as most precipitation falls in this period, while the demand is typically the highest in the dry season (late spring and summer) [5]. Furthermore, the state is prone to hydroclimatic extremes [1], with the most recent examples being the record-setting 2012–2015 drought and flooding in 2017. In the face of the geographically and temporally uneven distribution of water resources, the state traditionally relies on statewide and regional water storage and transfer projects, including the State Water Project (SWP) and the Central Valley Project (CVP), to redistribute water to meet multiple and often competing water management objectives [6]. However, the system was designed using hydroclimatic data of the first half of the 20th century. Since then, significant changes have been observed and reported, including increasing temperature, declining mountain snowpack, earlier snowmelt and streamflow peaking, higher percentage of precipitation falling as rainfall rather than snowfall, and increasing sea level, among others [7–17]. Those changes would likely amplify and accelerate in the future as the state's hydroclimate continues to change in a changing climate. In addition, as the population and economy continue to grow, natural hazards including extreme flooding and drought events pose a greater risk [18,19]. Those factors collectively make reliable water supply and drought and flood management in the state unprecedentedly challenging [20].

In light of their importance, many studies have focused on characterizing potential future hydroclimatic events in California [21–30]. These studies mostly used climate model projections from the Coupled Model Intercomparison Project Phase 3 (CMIP3) [31], which were produced more than a decade ago and do not represent the latest climate science. There are a few exceptions [21,24,25] that employed the latest climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) [32]. However, these studies generally focused on spatial scales not directly relevant to water resources management practices. For instance, Sun et al. [24] selected mountainous areas in Southern California as their study focus. In addition, the linear regression approach was generally used in trend assessment in those studies. The results of this method are largely affected by the starting and ending values of the study data and subject to the assumption of normality.

The objective of this study was to provide an assessment of the changes (from historical baseline) and trends of projected precipitation and temperature along with the trends in projected drought over California. This study extended beyond relevant previous studies in terms of: (1) focusing on the scale consistent with the water resources planning and management practices in the state; (2) using climate projections that reflects the latest climate science; and (3) applying the widely-used non-parametric Mann–Kendall approach in trend analysis. Compared to the traditional linear regression method, this method requires less assumption on data distribution and is less affected by the beginning and ending values of the study data. Specifically, the current study was built upon a previous study [14] that explored changes in historical precipitation, temperature, and drought in California. However, the current study differs from [14] in terms of study variables, study metrics, study method, study period, and study purpose. Particularly, this study aimed to offer insight into potential changes to California's hydroclimate on the scale meaningful for water resources management practices and to inform decision-makers in developing strategies to cope with these changes.
