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Decades of hypothesis-driven and/or first-principles research have beenapplied towards the discovery and explanation of the mechanisms that driveclimate phenomena, such as western African Sahel summer rainfall~variability.Although connections between various climate factors have been theorized, notall of the key relationships are fully understood. We propose a data-drivenapproach to identify candidate players in this climate system, which can helpexplain underlying mechanisms and/or even suggest new relationships, tofacilitate building a more comprehensive and predictive model of themodulatory relationships influencing a climate phenomenon of interest. Weapplied coupled heterogeneous association rule mining (CHARM), Lassomultivariate regression, and dynamic Bayesian networks to find relationshipswithin a complex system, and explored means with which to obtain a consensusresult from the application of such varied methodologies. Using this fusionof approaches, we identified relationships among climate factors thatmodulate Sahel rainfall. These relationships fall into two categories:well-known associations from prior climate knowledge, such as therelationship with the El Niño–Southern Oscillation (ENSO) and putativelinks, such as North Atlantic Oscillation, that invite further research

On the data-driven inference of modulatory networks in climate science: an application to West African rainfall