Tropical Cyclones (TCs) are singular storms causing intense wind, large waves, extreme water levels, and heavy rainfall. TCs prove every year to be one of the most destructive natural phenomena worldwide. The quantitative assessment of the hazards resulting from TCs (i.e., flooding and extreme winds) is challenging since satellite data are only available for recent decades, whereas older historical observations are incomplete and less accurate. In addition, long-term prediction through numerical weather forecasting is still limited. This often results in large uncertainties in the definition of TC hazards associated with events with longer return periods or in areas infrequently impacted by TCs. Even when this information is available, for example through statistical sampling of synthetic TC tracks, the numerical modelling of the associated hazards for all the different TC conditions can lead to computational costs which are often infeasible. Several methodologies that overcome the issues of accuracy and computational efficiency currently exist, but these are not generically applicable, and they tend to focus on specific areas only, for example where TCs typically make landfall. The main contribution of this paper is a novel methodology for the estimation and analysis of TC hydro-meteorological conditions and induced hazards. The method is generically applicable and maximizes accuracy while accounting for computational efficiency. Our approach identifies a smaller but representative set of TC tracks (RTCs) that preserves the information about extremes and the frequency of events of the larger population. The method is successfully applied and validated in a case study in the Bay of Bengal, using a set of synthetic TC tracks representing 1000 years of TC climate. For the best-performing configuration, the required number of scenarios and associated computational costs were reduced by 90% while maintaining accuracy in the simulated offshore storm surges, significant wave height, and windspeeds typically within 10% of the prediction based on the original full set of scenarios. This method is globally applicable and greatly improves the efficiency of TC-related hazard estimation, making it particularly valuable for areas with limited historical data.