<p>Modern monetary policy prioritizes price stability, a goal that depends on central banks' ability to produce accurate inflation forecasts. However, this task is increasingly complicated by climate change, which poses new risks to price stability. To address this challenge, this study examines the role of global climate in forecasting inflation in five major Asian economies: China, Japan, India, Indonesia, and South Korea. Premised in a Hybrid New Keynesian Phillips curve framework, the analysis employs supervised machine learning models on time-series data from 2000Q1 to 2023Q4. The findings reveal that global climate shocks significantly improve the predictive accuracy of inflation forecasts in all countries except Indonesia. Furthermore, global commodity prices act as a strong determinant of inflation across the region. Critically, the impact of these global factors exhibits notable heterogeneity between economies. The results carry important monetary and regional implications: central banks in China, Japan, India, and South Korea should integrate global climate variables into their forecasting and policy frameworks. The case of Indonesia, however, highlights that regional policy coordination must account for national economic structures to effectively manage the diverse inflationary consequences of climate change.</p>