We introduce a random forest approach to enable spreads' prediction in the primary catastrophe bond market. In a purely predictive framework, we assess the importance of catastrophe spread predictors using permutation and minimal depth methods. The whole population of non-life catastrophe bonds issued from December 2009 to May 2018 is used. We find that random forest has at least as good prediction performance as our benchmark-linear regression in the temporal context, and better prediction performance in the non-temporal one. Random forest also performs better than the benchmark when multiple predictors are excluded in accordance with the importance rankings. The latter indicates that random forest extracts information from existing predictors more effectively and captures interactions better without the need to specify them. The prediction accuracy results of random forest, and the minimal depth importance results are stable. There is only a small divergence between the  drivers of catastrophe bond spread in the predictive versus explanatory framework. The usage of random forest can speed up investment decisions in the catastrophe bond industry both for will-be issuers and investors.