Abstract
<title>Abstract</title> <p>Food-price surveillance in ASEAN must account for both weather-sensitive domestic supply and imported cost pressure. This paper constructs country-specific heat, rainfall-deficit, and excess-rainfall indicators from daily meteorological observations and aggregates them with a recency-weighted window before linking them to monthly food inflation. A pooled dynamic conditional-mean model and quantile regressions with country and seasonal indicators condition on inflation persistence, domestic price conditions, global food and energy prices, exchange-rate depreciation, and the post-pandemic shift. The mean estimates provide little evidence of a stable association between the climate-pressure indicators and food inflation, whereas global food-price inflation and currency depreciation remain informative. Across the conditional distribution, heat pressure becomes positive and statistically distinguishable from zero in elevated inflation states; rainfall deficits provide weaker evidence, and excess rainfall has no stable aggregate relationship. A future-weather placebo produces no meaningful conditional-mean association. The results indicate that representative-point climate measures are better interpreted as supplementary upper-tail monitoring signals than as causal estimates or stand-alone forecasts. The paper contributes a mixed-frequency framework for assessing distributional food-inflation risk and identifies the spatial, inferential, and real-time validation improvements required for operational use.</p>