For this example lets look at mean monthly rainfall rates over the region. These values are a very ’broad strokes’ descriptor, and many people have pointed out that such measurements are not the best indicator of the more nuanced variations and chronologies that can directly affect agricultural yields \cite{Tadross_2009} \cite{Ambrosino_2013} or the risk of floods/droughts. It’s been argued that for Southern Africa, increased temperatures are more consistently observed during El Niño events than changes in precipitation patterns, and that droughts are more a result of the interactions between high temperatures and preexisting conditions, than low rainfall alone \cite{Meque_2014}. However, often the records needed for more involved studies are not available, and so large scale precipitation estimates are commonly what are reported and extrapolated from. As such, it’s important to have some sense of these products. Here we will consider the Global Precipitation Climatology Project GPCP data set. As with the selected ENSO index, this is just one option of many. Working with multiple data sources is a complex problem, and will be addressed another time. There’s enough difficulty in reading the message of a single data source to keep us busy right now. Again though, it’s something to keep in mind; every part of the following analysis must be considered to be conditional on the choice of data set (the same as when choosing the El Niño definition).

So, lets look at a few sample months that were selected as occurring within El Niño periods. How are the estimated rainfall values different from what is typically observed on that given month [\ref{fig:elnino_samples}]?