Atlantic tropical cyclones (TC) have significant economic, societal and environmental impact in coastal regions. Advances in meteorological observing systems, dynamic and statistical models continue to improve our understanding of their genesis, development and evolution. For seasonal TC outlook, statistical methods are used to forecast TC activity (mainly the number of tropical storms and their intensity). Some of the large scale predictors proven valuable are: sea surface temperature anomalies (SSTA), vertical wind shear, Quasi-Biennial Oscillation (QBO), El Nino - Southern Oscillation (ENSO), and the phase of Atlantic Multidecadal Oscillation (AMO). In this study we use the NOAA Extended Reconstructed Sea Surface Temperature (SST), the TC counts in North Atlantic from the National Hurricane Center's North Atlantic hurricane database (HURDAT), and the NCEP/NCAR reanalysis data to investigate the statistical relationship between significant ENSO events and TC during the hurricane season. The statistical model is based on data from the time interval 1950 – 2009 and has predictive skill during intense ENSO events. Results show that forecasted ENSO SSTA can be used as predictors of TC in North Atlantic region. A series of potential uncertainties and model limitations are discussed, particularly due to possible climate alterations caused by global warming. Such issues are illustrated in the context of current efforts to understand climate predictability relevant to North Atlantic tropical storms.