The potential yields of deciduous fruit and nut trees are strongly affected by the climatic conditions that regulate dormancy and subsequent flowering. However, the realized yield at the end of the growing season depends on several additional factors including environmental conditions and management measures. These modulating factors dilute the direct effect of chill accumulation and frustrate efforts to forecast yields. Such forecasts are also limited by a lack of data, which complicates the assessment of the relationship between chill and yield. However, information on how diminishing chill impacts crop yield is crucial for farmers and other decision-makers, particularly in warm fruit and nut growing regions. We address this challenge by adopting a probabilistic approach to yield forecasting. This approach incorporates uncertainty and generates predictions that do not consist of precise numbers, but yield expectations expressed as probability distributions. We demonstrate a set of functions that we developed in the R programming language to generate forecasts of possible yields with given chill. We apply these methods to data sets of two sweet cherry (Prunus avium L.) cultivars ‘Lapins’ and ‘Brooks’.