The effect of the Southern Oscillation on daily precipitation in the southwestern United States is examined by using the Southern Oscillation Index (SOI) to perturb parameters of a stochastic daily precipitation model. Daily precipitation is modeled with a Markov chain-mixed exponential model and seasonal variability of model parameters is described by Fourier series. The hypothesized linkage between the SOI and the model parameters is of the form Gi(N,t)=Gi(t)+biS(N,t-&tgr;i) where Gi(N,t) is the perturbed parameter i for day t of year N, Gi(t) is the annually periodic parameter i for day t, bi is a coefficient, S is the SOI, and &tgr;i is a lag in days. Daily precipitation data for 27 stations in California, Nevada, Arizona, and New Mexico were analyzed. Perturbations of the logits of the dry-dry transition probabilities resulted in statistically significant improvements in the log likelihood function for 23 stations and perturbations of the mean daily rainfall resulted in significant increases for 18 stations. The most common lag identified was 90 days, suggesting the possibility of conditional simulations of daily precipitation. Seasonal effects were detected, confirming the results of previous analysis with groups of stations. ¿ American Geophysical Union 1993