Although the use of decomposition has won wide support as a means of improving the defensibility of judgmental forecasts, many decomposition techniques have encountered difficulties in ensuring the consistency of the respondent's probability statements. The more theoretically attractive methods have often become too complicated for practical assessment. In response to these difficulties, we present an approach that (1) aggregates judgmental forecasts and forecast adjustments based on partial probability information about conditioning scenarios and (2) guides the respondent into consistent replies by informing him about the judgments that are compatible with the earlier ones. The recent forecasting applications of hierarchical weighting are contrasted with the proposed approach. This is then illustrated with an example on the forecasting of hazardous emissions.