Botrytis leaf blight (BLB) caused by Botrytis squamosa is a major leaf disease of onion. Various forecasting systems have been developed to help growers manage the disease. To improve forecasting reliability, the influence of temperature and wetness duration on B. squamosa infection was quantified by inoculating onion leaves with a conidial suspension and incubating them under various combinations of temperature (10–30°C) and leaf wetness duration (0–84 h). Infection was measured as the number of lesions per cm2 of leaf and converted to the proportion of maximum infection (PMI). Regardless of leaf wetness duration, only a few lesions developed at 30°C and the number of lesions increased as the temperature rose from 10 to 20°C but decreased at 25°C. Between 10 and 25°C the number of lesions per cm2 of leaf area increased gradually with increasing leaf wetness duration from 12 to 72 h. Relative infection was modelled as a function of both temperature and wetness duration using a modified version of the Weibull equation, which provided a precise description of the response of B. squamosa (R2 = 0·88). To facilitate field validation, receiving operating characteristic curve analysis was performed to determine the accuracy of various sets of criteria for establishing the length of an infection event based on field weather data. The total number of leaf wetness and RH >90% hours over a 72 h period was the best criterion, regardless of the wetness interruption pattern (sensitivity = 90·91, specificity = 84·62, area under the receiving operating curve = 0·878). The model describing the relationship of PMI to temperature and leaf wetness duration, and field observations on airborne conidium concentration (ACC) were used to calculate the risk of infection (RIBLB) as RIBLB = PMI × ACC. In 2009 and 2010, this risk index was compared to the observed rate of BLB progress (RateBLB+5 days) during the following 5 days. There was a linear relationship between RIBLB and RateBLB+5 days indicating that this new risk indicator was reliable for predicting the risk of BLB development. These findings will help to improve the timing of fungicide applications for BLB management.