Zonda wind is a typical downslope windstorm over the eastern slopes of the Central Andes in Argentina, which produces extremely warm and dry conditions and creates substantial socioeconomic impacts. The aim of this work is to obtain an index for predicting the probability of Zonda wind occurrence. The Principal Component Analysis (PCA) is applied to the vertical sounding data on both sides of the Andes. Through the use of a binary logistic regression, the PCA is applied to discriminate those soundings associated with Zonda wind events from those that are not, and a probabilistic forecasting tool for Zonda occurrence is obtained. This index is able to discriminate between Zonda and non-Zonda events with an effectiveness close to 91％. The best model consists of four variables from each side of the Andes. From an event-based statistical perspective, the probability of detection of the mixed model is above 97％ with a probability of false detection lower than 7％ and a missing ratio below 1％. From an alarm-based perspective, models exhibit false alarm rate below 7％, a missing alarm ratio lower than 1.5％ and higher than 93％ for the correct alarm ratio. The zonal component of the wind on both sides of the Andes and the windward temperature are the key variables in class discrimination. The vertical structure of Zonda wind includes two wind maximums and an unstable lapse rate at midlevels on the lee side and a wind maximum at 700 hPa accompanied by a relatively stable layer near the mountain top.