Just how much can you legitimately extract from monthly statistics of daily weather parameters?  In this paper we present the utility and limitations of a simple weather generator (CLIGEN) which has over 750 registered users, most of whom are international.  CLIGEN's 4,000-plus station files of monthly parameters are within an average distance of 25 miles of any location in the U. S., providing the immediate means to generate daily weather values.  An automated way has been provided using a surrogate U. S. station, to build additional station files for any
location on the planet, from daily measurements of precipitation, and maximum and minimum temperature.  Although CLIGEN's simplicity permits it to be used with minimal data, a common criticism of CLIGEN is that 
it independently generates variables while in reality these variables
are interrelated.  If the CLIGEN output is used in a simulation model
which is highly sensitive two or more parameters on a daily basis, this
is probably a valid concern; however, the models using CLIGEN (WEPP,
WEPS, WEPP-SPUR) generally are not.  As a result, our primary goal is
to ensure that the statistical distributions produced, reflect reality
(measured data) as closely as possible.  If there are universal
relationships between weather data, our secondary goal is to capture
them within the logic of the model, so they can be utilized without
altering the model inputs, which would disrupt the existing CLIGEN
userbase.  We will discuss the extent to which we have achieved these
goals, and the implications to CLIGEN users.