Accurately predicting tropical cyclone (TC) activities is important for both hazard mitigation and adaptation. However, the number of TC genesis simulated by numerical models is sensitive to the model's horizontal resolution and how convection is being represented. A super-parameterized (SP) general circulation model (GCM), in which convection and sub-grid processes are explicitly simulated using a cloud-resolving microscale model embedded in each GCM grid column, but in a 2-D configuration, can achieve an explicit representation of convective processes while maintaining an acceptable computational cost. Here, we compared TC genesis using the traditionally convective parametrized Community Atmosphere Model v5.0 (using Zhang and McFarlane (1995) deep convective scheme and the University of Washington shallow convective scheme, hereafter referred to as CPCAM) with the SP CAM v5.0 (SPCAM); in particular, aquaplanet experiments with zonally symmetric sea surface temperature and perpetual summer insolation were conducted using the two models. It was found that SPCAM TC genesis was 3-4 times greater than that in CPCAM. Storm’s wind-pressure relationship was also improved, and they were stronger in SPCAM. Additionally, more “non-rotating convective clusters” and “TC seeds” were found in SPCAM compared to CPCAM. More frequent TC genesis in SPCAM was primarily related to weaker vertical wind shear (VWS) and secondarily to stronger 850-hPa absolute vorticity. Reduced VWS in the SPCAM was related to a weaker Hadley cell owing to less southern hemisphere subtropical low cloud and hence shortwave reflection. A wider and more northward ITCZ in SPCAM caused an increase in absolute vorticity, promoting TC genesis. There were also, stronger convective coupled equatorial wave activities in SPCAM than in CPCAM; better-simulated ER-waves appeared to induce a higher number of TC genesis events in the SPCAM environment. In the future, more sophisticated multiscale modelling frameworks can be employed to improve the simulation of extreme events in relation to the large-scale environment, while achieving better computational efficiency.