An appropriate choice of input models is critical for producing skillful HCCA forecasts. Input model sensitivity experiments indicate that although the ECMWF deterministic model has the largest positive impact to the skill of the HCCA track forecasts in both basins, there is year-to-year variability on the model performance and its impact on HCCA. HCCA forecasts are sensitive to the length of the training set. Although, it is difficult to know the exact number of training cases needed for a reliable HCCA forecast, the track forecasts seem to perform better with a longer training set length. The intensity forecasts seem to be less sensitive to the length of the training set length. Another major challenge for HCCA forecasts is when there is a mismatch between the magnitude of the individual model coefficient and the increment values. Accounting for instances when the size of an input model’s coefficient is disproportional to its contribution to HCCA is a major avenue for further improvement. One of the most challenging aspects of intensity forecasting is to reliably forecast rapid intensification (RI). The ability of HCCA to produce a forecast outside of the input models’ forecast envelope appears to give it an advantage for capturing RI forecasts over the equally-weighted variable consensus (IVCN).