Apply temporal filter
Year-on-year differences
This option replaces each data point with the dfference between the value of this year and the previous year. It acts as a (primitive) high-pass filter and is often used to eliminate to first order slow variations or trends. Note that higher-order effects, such as a slowly varying variance, are not removed.
Subtract mean of previous years
This is an extension of the year-on-year differences in which the mean of the previous N years is subtracted. (For N=1 they coincide). Again, these high-pass filters are used to remove low-frequency signals or trends. For N>1 the filter is more gentle than the year-on-year difference method and retains more low-frequency signal.
Average with previous years
The converse is to average with N previous years, making a N+1 year running mean. This primitive low-pass filter can be used to improve the signal/noise ratio of slowly-varying signals. Note that the degrees of freedom in the significance calculations are adjusted accordingly, so the signal may not be more significant after this low-pass filtering.
No overlap

If there is an option "no overlap" (currently only on the verification pages), this means that instead of the default sliding window, non-overlapping windows are taken. An example: if you have 100 years of data with a 10-yr running window ("average with [9] previous years"), you will normally get 90 or more data points that are not independent: years 1-10, 2-11, 3-12, ..., 91-100. With this option you will get 10 independent data points: years 1-10, 11-20, ..., 91-100.

One day I will implement more sophisticated low-pass, high-pass and band-pass filters.

See also the detrend option.