To illustrate, I'll go through the procedure with the two-day snowfall. For each snow season since 1919-20, we find the max snowfall total on consecutive calendar days. This gives us an "extreme values" time series. This seasonal maximum has ranged from 2.6" in 1952-53 to 26.9" in 1965-66. Now note that this maximum two-day total can occur at any point in the season, though realistically, late September to May. While the highest value typically occurs in the November to January time frame, it can be much earlier or later. For instance, in 2015-16, the highest two-day total was 13.5" September 29-30, while in 1991-92, the highest two-day total of 9.6" occurred in mid-May. So putting this in graphical form, on the left is the annual time series. It is clear just from inspection that there is no temporal trend, which simplifies the analysis. On the right is a histogram of those annual values. For many cold seasons, the highest two-day snow total is in the range of 5-9", but with a moderately long tail toward higher values.
Now we'll fit the data to a generalized extreme value distribution, and I plot the results as a function of return period, as in the graphic below. The observed seasonal maximum in this plot are rank ordered, the green line gives the best fit and the dashed lines are confidence intervals of the fit.