Stochastic weather generators are computer models used to simulate synthetic weather time series based on statistical characteristics of observed weather time series for a location of interest. The performance of weather generators should always be evaluated when applied over a new region, especially in a different climate zone. The Loess Plateau of China suffers from extensive soil erosion problems, mostly resulting from extreme precipitation events in an otherwise dry climate. The generation of accurate synthetic climate data (including the extremes) is the key to adequately assess the effect of soil and water conservation measures, especially in a changing climate. Accordingly, this work compares the ability of five commonly used stochastic weather generators (WGEN, CLIMGEN, CLIGEN, WeaGETS and LARSWG) to simulate daily precipitation and temperature for the Loess Plateau. The results show that the first-order Markov chain-based model satisfactorily simulates the precipitation occurrence, even though slightly better results can be achieved by using the second- and third-order Markov chain-based models. The semi-empirical distribution-based model shows a slightly better performance than the higher-order Markov chain-based models at generating dry spells, while it gives a worse performance in generating wet spells. The semi-empirical distribution-based LARSWG is consistently better than the parametric distribution-based weather generators at simulating daily precipitation amounts, and the three-parameter distribution-based models are consistently better than two-parameter distribution-based models, especially for generating extreme precipitation events. Overall, the performance of WeaGETS is generally superior to that of the other weather generators; it can be used for simulating daily precipitation, Tmax and Tmin for the Loess Plateau. While CLIGEN is not consistently a top performer, its reasonable representation of extreme precipitation events makes it suitable for soil erosion studies.