  We consider the spatio-temporal gridded daily diurnal temperature range (DTR)
data across India during the 72-year period 1951--2022. We augment this data
with information on the El Nino-Southern Oscillation (ENSO) and on the climatic
regions (Stamp's and Koeppen's classification) and four seasons of India.
  We use various matrix theory approaches to trim out strong but routine
signals, random matrix theory to remove noise, and novel empirical generalised
singular-value distributions to establish retention of essential signals in the
trimmed data. We make use of the spatial Bergsma statistics to measure spatial
association and identify temporal change points in the spatial-association.
  In particular, our investigation captures a yet unknown change-point over the
72 years under study with drastic changes in spatial-association of DTR in
India. It also brings out changes in spatial association with regard to ENSO.
  We conclude that while studying/modelling Indian DTR data, due consideration
should be granted to the strong spatial association that is being persistently
exhibited over decades, and provision should be kept for potential change
points in the temporal behaviour, which in turn can bring moderate to dramatic
changes in the spatial association pattern.
  Some of our analysis also reaffirms the conclusions made by other authors,
regarding spatial and temporal behavior of DTR, adding our own insights. We
consider the data from the yearly, seasonal and climatic zones points of view,
and discover several new and interesting statistical structures which should be
of interest, especially to climatologists and statisticians. Our methods are
not country specific and could be used profitably for DTR data from other
geographical areas.
