  The primary objective of this paper is to analyze a set of canonical spatial
patterns that approximate the daily rainfall across the Indian region, as
identified in the companion paper where we developed a discrete representation
of the Indian summer monsoon rainfall using state variables with
spatio-temporal coherence maintained using a Markov Random Field prior. In
particular, we use these spatio-temporal patterns to study the variation of
rainfall during the monsoon season. Firstly, the ten patterns are divided into
three families of patterns distinguished by their total rainfall amount and
geographic spread. These families are then used to establish `active' and
`break' spells of the Indian monsoon at the all-India level. Subsequently, we
characterize the behavior of these patterns in time by estimating probabilities
of transition from one pattern to another across days in a season. Patterns
tend to be `sticky': the self-transition is the most common. We also identify
most commonly occurring sequences of patterns. This leads to a simple seasonal
evolution model for the summer monsoon rainfall. The discrete representation
introduced in the companion paper also identifies typical temporal rainfall
patterns for individual locations. This enables us to determine wet and dry
spells at local and regional scales. Lastly, we specify sets of locations that
tend to have such spells simultaneously, and thus come up with a new
regionalization of the landmass.
