  Cloud-to-ground lightning strikes observed in a specific geographical domain
over time can be naturally modeled by a spatio-temporal point process. Our
focus lies in the parametric estimation of its intensity function,
incorporating both spatial factors (such as altitude) and spatio-temporal
covariates (such as field temperature, precipitation, etc.). The events are
observed in France over a span of three years. Spatio-temporal covariates are
observed with resolution $0.1^\circ \times 0.1^\circ$ ($\approx 100$km$^2$) and
six-hour periods. This results in an extensive dataset, further characterized
by a significant excess of zeroes (i.e., spatio-temporal cells with no observed
events). We reexamine composite likelihood methods commonly employed for
spatial point processes, especially in situations where covariates are
piecewise constant. Additionally, we extend these methods to account for
zero-deflated subsampling, a strategy involving dependent subsampling, with a
focus on selecting more cells in regions where events are observed. A
simulation study is conducted to illustrate these novel methodologies, followed
by their application to the dataset of lightning strikes.
