Detecting actual changes in the amount or frequency of precipitation received at a COOP station requires eliminating apparent "changes" that are the result of instrument drift, alterations in method of measurement and/or reporting, modification of the station's surroundings, etc. Change point detection is very challenging even when the measurements posses nice statistical properties such as normality, continuity, and homogeneity of variance. However, precipitation data do not possess nice statistical properties. In fact, the occurrences of precipitation are relatively infrequent and when precipitation does occur, the measurements tend to be skewed. For these sorts of measurements, use of standard change point methods is not recommended.

Fortunately, the COOP network is fairly dense. Each station has several neighbors also measuring precipitation. The relationship between frequency and amount of precipitation at each station is compared to its neighbors for each month over the entire period of record. Thus the empirical distribution and time series of the measures of association between stations are obtained. New measurements can be compared to these to determine if the recent measures are "typical" or not. If not, the station can be flagged as possibly having experienced a change and further checks can be performed. Preliminary results of these comparisons for Iowa are presented. Additionally, these comparisons are performed on simulated data to determine the period of time required to detect changes of different magnitudes.