The Santa Ana winds represent a high-impact weather event owing to the intimate relationship between the extremely dry, fast winds and the wildfire threat. The winds can be locally gusty, particularly in the complex terrain of San Diego county, where the airflow has characteristics of downslope windstorms. These winds can cause and/or rapidly spread wildfires, the threat of which is particularly acute during the autumn season before the onset of winter rains. It remains a day-to-day challenge to accurately predict wind gust speed, especially in the mountainous regions.Our study employs large physics ensembles composed of high-resolution simulations of severe downslope windstorms that involve an exhaustive examination of available model physical parameterizations. Model results are calibrated and validated against the San Diego Gas and Electric (SDG&E) mesonet observations, a dense, homogenous, and well-positioned network with uniform high quality. Results demonstrate model horizontal resolution, model physics, random perturbations and landuse database can have a material effect on the strength, location and timing of Santa Ana winds in real-data simulations. A large model physics ensemble reveals the land surface model to be most crucial in skillful wind predictions, which are particularly sensitive to the surface roughness length. A surprisingly simple gust parameterization is proposed for the San Diego network, based on the discovery that this homogeneous mesonet has a nearly invariant network-averaged gust factor. The gust forecast technique is of special interest in the context of routine weather combined with atmospheric humidity and fuel moisture information. A real-time wildfire threat warning system, the Santa Ana Wildfire Threat Index (SAWTI), has been developed to effectively communicate the upcoming Santa Ana wind strength with respect to the anticipated fire danger to first responders and the public. In addition to the wind and gust forecast techniques, attempts have been made to skillfully model two essential elements that SAWTI is heavily dependent on, i.e., the live fuel moisture and the greenness, using meteorological information. The models developed can skillfully determine these essential elements from both forecast and reanalysis data.