Existing literature has confirmed that severe weather events have a negative impact on travel behavior. Considering the features of long distances and long durations, intercity travel may be affected by the weather at the origin (O) point and at the destination (D) point. This paper aims to reveal the relationship between weather and OD volume of intercity travel by additional considerations of the weather difference between OD points and the lagging effect of various severe weather conditions. Travel data from toll collection system of freeway network in Shandong Province of China is extracted to investigate the weather effect on intercity travel demand. Three types of real-time weather variables (continuous variables of basic weather, dummy variables of severe weather, and weather difference variables between OD points) and two types of non-real-time weather variables (lagging variables and advance variables of severe weather) are combined to systematically analyze the weather effect. The results show that, temperature value and wind speed have no significant impact on intercity travel. Hot day, cold day, foggy day, strong breeze, heavy rain, rainfall, snowfall and visibility have significant impacts on freeway OD volume. Among these weather events, fog, heavy rain and snow usually have the most severe impact. Except for the weather at the O point, the differences in rainfall and snowfall between OD points have significant impacts on intercity travel demand, while the differences in temperature, wind speed, visibility between OD points do not. In addition, snow and heavy rain have the lagging and advance effects on freeway OD volume. The advance effect of snow and heavy rain lasts for one day and the lagging effect lasts for two days. Intercity travel demand is very sensitive to adverse weather on weekend days and during the afternoon. Comparative analysis of multiple OD pairs indicates that intercity travelers with less relevant experience are more sensitive to adverse weather. Findings from the paper will provide transport institutions with a practical guide for systematically investigating the weather effect on intercity travel demand. It can be applied in the intelligent transportation system to provide long-distance travelers with more accurate weather information and traffic guidance services.