The rapid development of digital and intelligent technologies in nuclear power plants has improved operational efficiency, but human actions are still essential in preventing abnormal events and radiological hazards. This study proposed a methodology to investigate the continuous probability distributions of operator required time and available time, employing Monte Carlo (MC) sampling to calculate Human Error Probability (HEP). Applied to a Medium-Break Loss-of-Coolant accident (MLOCA) scenario, the approach overcame the limitation of traditional probabilistic safety assessment (PSA) models that assumed constant time values. The calculated HEP for operators performing cooling and depressurization instrumentation (C&DI) operation is 0.0261, which is much higher than the conventional PSA model’s result of 1.71 × 10⁻2. By explicitly analyzing the distributions of required time and available time, this method provided a more precise characterization of operator behavior under accident conditions. The results of case study revealed that the increased HEP values with distributions of required time and available time highlighted the critical role of time dynamics in human reliability within complex systems. This research established foundations for optimizing nuclear power plant operator training, accident management procedures, and emergency strategies. The study emphasized the necessity of integrating temporal continuity into risk assessment, advancing nuclear safety and operational reliability.