Predicting Fire Season Intensity in Maritime Southeast Asia with Interpretable Models

15 There have been many extreme fire seasons in Maritime Southeast Asia (MSEA) 16 over the last two decades, a trend which will likely continue, if not accelerate, due to cli17 mate change. Fires, in turn, are a major driver of atmospheric carbon monoxide (CO) 18 variability, especially in the Southern Hemisphere. Previous studies have explored the 19 relationship between climate variability and fire counts, burned area, and atmospheric 20 CO through regression models that use climate mode indices as predictor variables. Here 21 we model the connections between climate variability and atmospheric CO at a level of 22 complexity not yet studied and make accurate predictions of atmospheric CO (a proxy 23 for fire intensity) at useful lead times. To do this, we develop a regularization-based sta24 tistical modeling framework that can accommodate multiple lags of a single climate in25 dex, which we show to be an important feature in explaining CO. We use this framework 26 to present advancements over previous modeling e↵orts, such as the inclusion of outgo27 ing longwave radiation (OLR) anomalies, the use of high resolution weekly data, and a 28 stability analysis that adds weight to the scientific interpretation of selected model terms. 29 We find that the El Niño Southern Oscillation (ENSO), the Dipole Mode Index (DMI), 30 and OLR (as a proxy for the Madden-Julian Oscillation) at various lead times are the 31 most significant predictors of atmospheric CO in MSEA. We further show that the model 32 gives accurate predictions of atmospheric CO at leads times of up to 6 months, making 33 it a useful tool for fire season preparedness. 34