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CLIGEN is the only weather generator that generates internal storm patterns, which are required by many agricultural system models such as the Water Erosion Prediction Project (WEPP) model. The lack of correlation between CLIGEN-generated storm variables may limit those models' abilities to predict surface runoff and soil erosion. The objectives of this study were to: (1) test a distribution-free method for inducing desired rank correlation between generated storm variables, and (2) compare WEPP-predicted runoff and soil loss using measured vs. variously generated storm patterns on eight U.S. sites. Four climate files containing four storm patterns (measured, original uncorrelated CLIGEN output, correlated CLIGEN output, and correlated output with exponentially generated storm durations), along with measured soil, slope, and crop management on each site, were used as input to WEPP. The distribution-free approach was simple to use and capable of inducing desired rank correlation between storm depth and duration and consequently between storm depth and relative peak intensity. Original CLIGEN output after inducing desired correlation considerably improved WEPP runoff and soil loss predictions on most sites where strong correlation between storm depth and duration existed. On average, the relative errors averaged over all sites were reduced from 15.0% to 4.6% for runoff prediction and from 11.1% to 1.5% for soil loss prediction. The use of exponentially distributed storm duration, compared with the original CLIGEN output, doubled the overall relative error for soil loss prediction due to the undesirable alteration of relative peak intensity estimates. Overall results indicate that for better runoff and soil loss prediction, correlated CLIGEN output should be used on sites where strong correlation between storm depth and duration exists. [ABSTRACT FROM AUTHOR]