Conditioning stochastic daily precipitation models on total monthly precipitation

Abstract

Chain‐dependent stochastic daily precipitation models are fit to dry, near‐normal, and wet subsets of monthly total precipitation data, using category definitions consistent with the 30‐day forecasts issued by the Climate Analysis Center of the National Oceanic and Atmospheric Administration. The resulting models are compared to those derived unconditionally from entire data records. It is found that for the 10 selected North American stations investigated, the unconditional models produce distributions of total monthly precipitation having too few dry and wet months as compared to the observations, while appropriate probability mixtures of the three conditional models can accurately reproduce the climatological distributions of total monthly precipitation. Application of the conditional precipitation models to generation of daily data consistent with certain longer‐term aspects of the observations is also illustrated.