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Abstract The cooling effect of the ocean on the Southern California coastal zone is investigated using a high‐resolution (4‐km) gridded surface meteorological data set (gridMET) of daily maximum temperature (Tmax), with focus on summer mean conditions, taken as the July–August–September (JAS) average. An empirical orthogonal function analysis reveals a coastal mode of JAS temperature covariability, distinct from a more energetic inland mode, that captures Tmax averaged across the Southern California coastal plain. The coastal mode temperature correlates significantly with, and has similar amplitude to, regional sea surface temperature (SST). High (low) summer land and sea surface temperatures, as well as inversion layer temperature differences, are associated with decreases (increases) of northerly coastal wind speeds and coastal cloudiness. The number of extreme heat days on land increases as regional SST increases (4.3 days °C⁻¹), with heat wave days 10 times more likely during peak warm versus cool coastal mode years. The coastal zone was notably warmer and heat wave days peaked during the well documented marine heat wave events of 2014/15 and 2018 off Southern California. The marine variability associated with the coastal mode also has strong expression off the Baja California peninsula, presumably due to strong covarying winds in that area. As in previous studies, higher ocean temperatures are attributed to weaker summer winds, with associated reductions in ocean surface heat loss, coastal upwelling, and cloudiness. 

Abstract Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from extending lead times of skillful prediction for these and other types of extreme weather patterns. Here we describe a methodology for subseasonal prediction of impactful winter weather in California, including ARs, SAWs and heat extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1–4 lead. This methodology uses dynamical model information considered most reliable, that is, planetary/synoptic‐scale atmospheric circulation, filters for dynamical model error/uncertainty at longer lead times and increases the sample of likely outcomes by utilizing the full historical record instead of a more limited suite of dynamical forecast model ensemble members. We demonstrate skill above climatology at subseasonal timescales, highlighting potential for use in water, health, land, and fire management decision support. 

Abstract Low‐level stratiform clouds modulate California's coastal climate during the warm season. Previous work describing the seasonal and daily variability of coastal low cloudiness (CLC) suggests that in July, August, and September southern California's CLC is under the influence of an additional driver, which has less impact in northern California. In this work, we introduce the link in which free‐tropospheric moisture dictated by North American Monsoon (NAM) processes can impact southern California CLC. We use in situ and remote sensing observations, as well as reanalysis and single column model simulations to identify and investigate this previously missing component. We find that monsoonal moisture advected by southeasterly flow from the core NAM region into southern California reduces CLC by diminishing cloud‐top longwave cooling. To add to an already complex brew of known factors influencing coastal cloudiness, another one is hereby introduced and should be accounted for in future work.