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Heatwaves and bushfires cause substantial impacts on society and ecosystemsacross the globe. Accurate information of heat extremes is needed to supportthe development of actionable mitigation and adaptation strategies. Regionalclimate models are commonly used to better understand the dynamics of theseevents. These models have very large input parameter sets, and the parameterswithin the physics schemes substantially influence the model's performance.However, parameter sensitivity analysis (SA) of regional models for heatextremes is largely unexplored. Here, we focus on the southeast Australianregion, one of the global hotspots of heat extremes. In southeast AustraliaWeather Research and Forecasting (WRF) model is the widely used regional modelto simulate extreme weather events across the region. Hence in this study, wefocus on the sensitivity of WRF model parameters to surface meteorologicalvariables such as temperature, relative humidity, and wind speed during twoextreme heat events over southeast Australia. Due to the presence of multipleparameters and their complex relationship with output variables, a machinelearning (ML) surrogate-based global sensitivity analysis method is consideredfor the SA. The ML surrogate-based Sobol SA is used to identify the sensitivityof 24 adjustable parameters in seven different physics schemes of the WRFmodel. Results show that out of these 24, only three parameters, namely thescattering tuning parameter, multiplier of saturated soil water content, andprofile shape exponent in the momentum diffusivity coefficient, are importantfor the considered meteorological variables. These SA results are consistentfor the two different extreme heat events. Further, we investigated thephysical significance of sensitive parameters. This study's results will helpin further optimising WRF parameters to improve model simulation.

Machine Learning based Parameter Sensitivity of Regional Climate Models -- A Case Study of the WRF Model for Heat Extremes over Southeast Australia