This study introduces parametric Group Method of Data Handling (GMDH) into regional NSW, Australia, for the first time with the intent to assess the impact of climate change through rainfall modelling of publicly available data. The modelling presented the opportunity to investigate improvements to GMDH for rainfall modelling, with an empirical assessment undertaken. State variable distribution, their classification within the context of fuzzy, and the need to integrate Zadeh’s principle of incompatibility into the GMDH modelling format are all assessed. The mathematical foundations of GMDH are discussed within the heuristic framework of data partitioning, partial description synthesis and the limitations of least squares coefficient determination, Gödel’s incompleteness theorem and the necessity for an external criterion in the selection procedure for the Ivakhnenko polynomial. Methods for modelling improvement include the potential for hybridisation with least square support vector machines (LSSVM), the application of Kalman filters for parameter estimation, and the combination with signal processing techniques; ensemble empirical mode decomposition (EEMD), wavelet transformation (WT), and wavelet packet transformation (WPT) being investigated as is the implementation of enhanced GMDH (eGMDH) and fuzzy GMDH (FGMDH). The inclusion of exogeneous data is also discussed and whether application presents within the GMDH modelling paradigm. The study concludes with recommendations made to enhance the potential for future rainfall modelling study success.