Energy consumption, agricultural activities and comfort in building design are all related to temporal temperature variations. Truncation of the temperature series at a constant base temperature level leads to surpluses and deficits as deviations. Surpluses are instances for cooling and deficits for heating. In meteorology and heat engineering these are referred to as cooling and heating degree-days, respectively. Since the temperature records are random in character their future predictions are necessary through statistical and probabilistic methods. In this paper, the degree-days are assumed to have a normal probability distribution function and therefore, their averages rind standard deviations are considered sufficient for modeling cooling and heating degree-day amounts. Theoretical derivations are presented for degree-day risk calculations in their general forms and a simple implementation is given for two cities in Turkey.