Regression model for generating time series of daily precipitation amounts for climate change impact studies

The precipitation amounts on wet days at De Bilt ( the Netherlands) are linked to temperature and surface air pressure through advanced regression techniques.
Temperature is chosen as a covariate to use the model for generating synthetic time series of daily precipitation in a CO2 induced warmer climate. The precipitation-temperature dependence can partly be ascirbed to the phenomenon that warmer air can contain more moisture. Spline functions are introduced to reproduce the non-monotonous change of the mean daily precipitation amount with temperature.
Because the model is non-linear and the variance of the errors depends on the expected response, an iteratively reweighted least-squares technique is needed to estimate the regression coefficients. A representative rainfall sequence for the situation of a systematic temperature rise is obtained by multiplying the precipitation amounts in the observed record with a temperatue dependent factor based on a fitted regression model. For a temperature change of 3ºC (reasonable quess for a doubled CO2 climate according to the present-day general circulation models) this results in an increase in the annual average amount of 9% (20% in winter and 4% in summer). An extended model with both temperature and surface air pressure is presented which makes it possible to study the additional effects of a potential
systematic change in surface air pressure on precipitation.

Key words: Climate change, daily precipitation modelling, generalized linear models, iteratively reweighted least squares, spline functions.