Natural disasters pose significant threats, necessitating effective warning systems and robust historical data for climate research. Traditional methods struggle to collect comprehensive data, especially for localized events like flash floods, leading to data scarcity. Groundsource addresses this challenge by extracting verified information from unstructured news reports using advanced AI, specifically the Gemini Large Language Model. This methodology produces a detailed global dataset of flash floods, encompassing 2.6 million historical events across over 150 countries. The process involves classifying flood reports, determining precise timing, and mapping locations using Google Maps Platform. Technical validation shows high accuracy in event extraction, demonstrating the reliability of Groundsource. This new dataset vastly expands coverage compared to existing archives, capturing both high-impact and localized events. The resulting data enables near-global urban flash flood forecasts up to 24 hours in advance, now integrated into Google's Flood Hub. Groundsource’s success highlights the potential of LLMs to transform unstructured data into a crucial scientific baseline for various hazards. This approach can be extended to other disasters, contributing to a more resilient and prepared future for communities worldwide.