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PG&E operates over 130,000 KM of overhead electric lines in Northern and Central California that can fail during weather events and present risk of wildfire ignition when fuels are receptive. Operational grid mitigations such as proactive deenergizations and sensitive relay settings effectively reduce ignition probability but lead to increased customer outages. Thus, it is imperative to deploy accurate operational risk models to drive daily decisions to mitigate risk. This talk focuses on our fifth-generation machine learning outage, ignition and wildfire probability models trained by coupling a WRF-based 30+ year climatology of weather, dead and live fuel moisture data with sub-daily fire occurrence and PG&E outage and ignition data. Presented by Scott Strenfel as part of the 2026 California Fire Science Seminar Series.