The paper reports results of non-parametric analysis of peanut, corn, and cotton yield distributions by the ElNino Southern Oscillation (ENSO) phases in the Southeastern U.S. For validation purposes, the historical yield data is complemented by a set of simulated peanut yields generated using daily weather data. The hypothesis, justified by the observed South-Eastern climate differences and research on ENSO cycles and planting dates, is that different climate conditions during ENSO cycles translate into different yield distributions and, therefore, insurance premiums (loss to coverage ratios). Kernel density estimates of historical county yield data show consistent patterns in the actuarially fair rate schedules grouped by ENSO phases and geographical areas. In particular, corn and cotton yield insurance premiums appear to be the most dependent on the ENSO phases and are the highest, regardless of coverage, during ElNino and the lowest during LaNina. Peanut premiums are higher during Neutral years and lowest during LaNina. The results appear to be robust to the transformations used to make the yield series stationary. While these dependencies do not necessarily correspond to the precipitation and solar radiation characteristics of the corresponding ENSO cycles in the Southeastern US, drawing direct analogies with yield variability is premature as many less documented factors, like the spacing of sunny and rainy days, may be just as important. The comparisons of the empirical and simulated peanut yield distributions show that they are similar in many ways and that the dissimilarities can be explained by known factors. These findings should be more relevant for the area yield insurance as opposed to the APH arrangements as the yield data used in designing contracts for the former reflects the systemic risk more influenced by climate than by the farm-level, basis risk factors accommodated in the APH plans.