  Weather is a key production factor in agricultural crop production and at the
same time the most significant and least controllable source of peril in
agriculture. These effects of weather on agricultural crop production have
triggered a widespread support for weather derivatives as a means of mitigating
the risk associated with climate change on agriculture. However, these products
are faced with basis risk as a result of poor design and modelling of the
underlying weather variable (temperature). In order to circumvent these
problems, a novel time-varying mean-reversion L\'evy regime-switching model is
used to model the dynamics of the deseasonalized temperature dynamics. Using
plots and test statistics, it is observed that the residuals of the
deseasonalized temperature data are not normally distributed. To model the
non-normality in the residuals, we propose using the hyperbolic distribution to
capture the semi-heavy tails and skewness in the empirical distributions of the
residuals for the shifted regime. The proposed regime-switching model has a
mean-reverting heteroskedastic process in the base regime and a L\'evy process
in the shifted regime. By using the Expectation-Maximization algorithm, the
parameters of the proposed model are estimated. The proposed model is flexible
as it modelled the deseasonalized temperature data accurately.
