A multifactor (complex, abnormal, critical, etc.) s ituation can quickly develop in flight as a result of dynamic mixing and cross-coupling of demanding weather conditions, pilot errors, mechanical malfunctions, and hidden design flaws. These operational factors are typically linked by strong cause-and-effect relationships, often lea ding the ‘operator (human pilot, automaton) ‐ aircraft’ system to a ‘chain reaction’ type acciden t. At present, designers, flight test engineers/pilots , regulators, instructors, and line pilots have limited resources to address multifactor flight cas es during design, test, certification, training and operational phases of the vehicle’s life cycle. The main difficulties are combinatorial (‘the curse of dimensionality’), technical, time and budget con straints. The developed methodology includes the notions of flight situation scenario (a micro-structural model of flight), multifactor situational tree (a m acro-structural model of flight), operational factor, tree ‘genotype’, operational hypothesis, sa fety spectrum, and safety window. Using this conceptual framework, it becomes possible to automatically plan, map and explore a multifactor operational domain of flight in autonomous fast-tim e simulation experiments using the system model. Situational trees are economic data structures in t erms of computer memory and processing time. They can be used to screen (thread) a multifactor o perational domain of flight, detect potentially dangerous combinations of operational factors (acci dent precursors) and map its ‘safety topology’ using user-friendly knowledge formats. The result is a bird eye’s view picture of the safety topology of the multifactor operational doma in of flight of interest. In this paper, the background theory, algorithms, d ata structures and application examples of the developed technique will be introduced using realis tic scenarios of complex flight situations.