The recent multiyear drought over California was characterized by large precipitation deficits and abnormally high temperatures during both wet and dry seasons. This study investigates and quantifies the contributions of precipitation and temperature anomalies to the development of the multiyear drought with a set of modeling experiments where the anomalies are either removed or randomly replaced with other historical observations. The study reveals that precipitation deficits have been largely responsible for producing the extreme agricultural drought (i.e., large soil moisture deficits) while warmer temperatures have only marginally intensified the drought. However, the warmer temperatures over the high‐elevation areas during the wet season have contributed equally or more than the precipitation deficits to the reduction of snowpack. The interplay between temperature and precipitation anomalies in space and time also appears to be important for the drought development.

In winter weather, precipitation type is a pivotal characteristic because it determines the nature of most preparations that need to be made. Decisions about how to protect critical infrastructure, such as power lines and transportation systems, and optimize how best to get aid to people are all fundamentally precipitation-type dependent. However, current understanding of the microphysical processes that govern precipitation type and how they interplay with physics-based numerical forecast models is incomplete, degrading precipitation-type forecasts, but by how much? This work demonstrates the utility of crowd-sourced surface observations of precipitation type from the Meteorological Phenomena Identification Near the Ground (mPING) project in estimating the skill of numerical model precipitation-type forecasts and, as an extension, assessing the current model performance regarding precipitation type in areas that are otherwise without surface observations. In general, forecast precipitation type is biased high for snow and rain and biased low for freezing rain and ice pellets. For both the North American Mesoscale Forecast System and Global Forecast System models, Gilbert skill scores are between 0.4 and 0.5 and from 0.35 to 0.45 for the Rapid Refresh model, depending on lead time. Peirce skill scores for individual precipitation types are 0.7–0.8 for both rain and snow, 0.2–0.4 for freezing rain and freezing rain, and 0.25 or less for ice pellets. The Rapid Refresh model displays somewhat lower scores except for ice pellets, which are severely underforecast, compared to the other models.

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