Tropical cyclones (TCs) are important extreme weather phenomena that have a significant negative impact on humans, infrastructure, and society. Multidecadal simulations from high resolution regional and global climate models are now being used to better understand how TC statistics change due to anthropogenic climate change. Although various studies have been conducted on the climatology of tropical cyclone genesis (TCG), their analyses are often limited in that they focus on basin-specific models, discard high resolution data in favor of aggregate measures, or bias their investigation towards variables and metrics that are motivated by mathematical physics. Coarse GCM resolution and the unsuitability of the Genesis Potential Index (GPI) for making future TC projections highlight the need for additional tools.

Previous work has shown that a statistical model can accurately predict TCG in the Community Atmospheric Model (CAM) Version 5.1. L1-regularized logistic regression (L1LR) was successfully applied to distinguish between TCG events and non-developing storms with high accuracy. In this study, we extend this framework by investigating the differences between statistical models of TCG in differing climate scenarios. We compare the statistical characterization of TCG in three climate scenarios: a warmer world, the world as it is, and a natural world where the greenhouse gas effect is removed. Our results agree with research that exhibits slight decreases in TC activity in a warmer world and increases in the natural world. Since the TCG probability model remains fairly similar for each climate, this suggests that the changes in TC counts might be attributable to a change in the number of events where favorable conditions occur.