Modelling observed meteorological elements can be useful. For instance, modelling rainfall has
been an interest for many researchers. In a previous research, trend surface analysis was used and
it was indicated that the residuals might spatially be correlated. When dealing with spatial data, any
modelling technique should take spatial correlation into consideration. Hence, in this project, fitting
of spatial regression models, with spatially correlated errors to the annual mean relative humidity
observed in Peninsular Malaysia, is illustrated. The data used in this study comprised of the annual
mean relative humidity for the year 2000-2004, observed at twenty principal meteorological stations
distributed throughout Peninsular Malaysia. The modelling process was done using the S-plus
Spatial Statistics Module. A total of twelve models were considered in this study and the selection
of the model was based on the p-value. It was found that a possible appropriate model for the
annual mean relative humidity should include an intercept and a term of the longitude as covariate,
together with a conditional autoregressive error structure. The significance of the coefficient of the
covariate and spatial parameter was established using the Likelihood Ratio Test. The usefulness
of the proposed model is that it could be used to estimate the annual mean relative humidity at
places where observations were not recorded and also for prediction. Some other potential models
incorporating the latitude covariate have also been proposed as viable alternatives.