In this study we develop a systematic methodology to analyze cyclogenesis in high resolution climate model simulations. The motivation for this study is to understand how cyclones develop in simulations with the objective of improving the theoretical foundations of cyclogenesis. We use the toolkit for extreme climate analysis (TECA) [Prabhat et al., ICCS 2012] to detect and track cyclones (TCs) in recent high resolution simulations (25km) of current day and climate change scenarios [Wehner et al, J Climate 2015], as well as reanalyses. We systematically adjust the tracking criteria to identify developing and non-developing TCs. The detection and tracking criteria are based on (i) the local relative vorticity maximum being above a certain value, (ii) the colocation of vorticity maximum, surface pressure minimum and warm core temperature maximum, (iii) surface pressure gradient around the storm center to be above a certain value, and (iv) temperature gradient around the warm core center to be above a certain value. To identify non-developing TCs, we systematically characterize the sensitivity of cyclone detection to these criteria using a principal component analysis on the criteria. First, we composite vorticity, pressure and temperature fields around the start of each cyclone’s trajectory. Second, we find the covariance of pairs of thresholded variables, for example, vorticity and pressure gradient. Finally, we construct a cross-correlation matrix with these covariances and find the eigenvectors. The eigenvector corresponding to the largest eigenvalue describes the direction of maximum sensitivity.We simultaneously lower thresholds along the direction of maximum sensitivity, which results in an increase in the number of TC-like systems and trajectory lengths compared to the baseline case. We contrast the behavior of developing and non-developing TCs by constructing multivariate joint PDFs of various environmental conditions along their trajectories. We also compute the standard genesis potential index, max potential intensity and ventilation index along these trajectories. Existing indices do not appear to capture the probability of cyclogenesis accurately. Multivariate joint PDF analysis could provide the foundation to develop a more comprehensive predictive tool for cyclogenesis.