Continuous probability forecasts are useful for making expected value calculations and informing decisions. This study provides tools for estimating continuous probability distributions given discrete probabilistic forecasts. We show that these continuous distributions can be aggregated to yield consensus distributions that consistently outperform the average forecaster. Modeled continuous distributions performed equivalently to explicit forecasts at the individual level. We evaluated different methods for aggregating continuous distributions. The best methods achieved increases in performance accuracy similar to the benefits of aggregating discrete forecasts. This result suggests the potential to apply many of the known techniques for aggregating and weighting discrete forecasts to the continuous domain. This experiment clearly demonstrates that even with diverse forecasting topics and response quality, consensus methods can efficiently extract signal from the crowd noise and produce useful forecasts of probability distributions.