Many efficient forecasting models have been found to fail or show low skill due to the changes in the predictor–predictand relationship with the changes in global climate. An attempt has been taken to develop a climate change resilient heatwave prediction model using machine learning (ML) algorithms known as Support Vector Machines (SVM), random forest and artificial neural network. The National Centres for Environmental Prediction/National Centre for Atmospheric Research reanalysis data of ocean-atmospheric variables were used as the predictors of ML models for forecasting the number of heatwave days (HWDs) in the summer of Pakistan. An SVM based recursive feature elimination method was used to select the skilful predictors. The ML models were developed by considering a moving window of 29 years with a time step of 5 years to incorporate the changes in the relation of HWDs with its predictors due to climate change. The result showed changes in the relationship of HWDs with all the ocean-atmospheric variables considered in this study as probable predictors, which indicates the necessity of forward-rolling approach proposed in this study for the development of climate change resilient forecasting model. The relative performance of ML showed the higher capability of SVM to predict HWDs with an %NRMSE of 36, R² of 0.87, md score of 0.76 and an rSD of 0.88 during the validation period. The result revealed the potential of SVM model to be used for reliable forecasting of heatwaves in the context of climate change.

由于与全球气候变化的预测因子和预测因子之间的关系发生了变化，因此发现许多有效的预测模型都将失败或显示出较低的技能。已经尝试使用称为支持向量机（SVM），随机森林和人工神经网络的机器学习（ML）算法来开发气候变化适应性热浪预测模型。国家环境预测中心/国家大气研究中心对海洋大气变量的再分析数据被用作ML模型的预测器，用于预测巴基斯坦夏季的热浪天数（HWD）。基于支持向量机的递归特征消除方法被用来选择熟练的预测因子。通过考虑29年的移动窗口和5年的时间步长来开发ML模型，以纳入由于气候变化而导致的HWD与其预测变量之间的关系变化。结果表明，HWDs与本研究中所有被视为可能的海洋大气变量之间的关系发生了变化，这表明本研究中提出的前滚方法对于发展气候变化弹性预测模型的必要性。ML的相对性能显示SVM预测％NRMSE为36，R的HWD的能力更高 这表明在本研究中提出的前滚方法对于发展气候变化弹性预测模型的必要性。ML的相对性能显示SVM预测％NRMSE为36，R的HWD的能力更高 这表明在本研究中提出的前滚方法对于发展气候变化弹性预测模型的必要性。ML的相对性能显示SVM预测％NRMSE为36，R的HWD的能力更高在验证期间，其中^2个为0.87，md得分为0.76，rSD为0.88。结果表明，在气候变化的背景下，支持向量机模型可用于热浪的可靠预测。