  The Standardized Precipitation Index (SPI) is used to indicate the
meteorological drought situation - a negative (or positive) value of SPI would
imply a dry (or wet) condition in a region over a period. The climate system is
an excellent example of a complex system since there is an interplay and
inter-relation of several climate variables. It is not always easy to identify
the factors that may influence the SPI, or their inter-relations (including
feedback loops). Here, we aim to study the complex dynamics that SPI has with
the SST, NINO 3.4 and Indian Ocean Dipole (IOD), using a machine learning
approach. Our findings are: (i) IOD was negatively correlated to SPI till 2008;
(ii) until 2004, SST was negatively correlated with SPI; (iii) from 2005 to
2014, the SST had swung between negative and positive correlations; (iv) since
2014, we observed that the regression coefficient ($\delta$) corresponding to
SST has always been positive; (v) the SST has an upward trend, and the positive
upward trend of $\delta$ implied that SPI has been positively correlated with
SST in recent years; and finally, (vi) the current value of SPI has a
significant positive correlation with a past SPI value with a periodicity of
about 7.5 years. Examining the complex dynamics, we used a statistical machine
learning approach to construct an inferential network of these climate
variables, which revealed that SST and NINO 3.4 directly couples with SPI,
whereas IOD indirectly couples with SPI through SST and NINO 3.4. The system
also indicated that Nino 3.4 has a significant negative effect on SPI.
Interestingly, there seems to be a structural change in the complex dynamics of
the four climate variables, some time in 2008. Though a simple 12-month moving
average of SPI has a negative trend towards drought, the complex dynamics of
SPI with other climate variables indicate a wet season for western Australia.
