The frequency of extreme high temperature events has increased against the backdrop of global warming,posing serious risks to natural ecosystems,socio-economic development,and human safety.The Eurasian mid-high latitudes,or core regions of the Belt and Road area,feature fragile ecological environments highly susceptible to climate change,with limited adaptive capacities to extreme weather events.In recent decades,the frequent occurrence of extreme high-temperature events in these latitudes has resulted in tens of thousands of fatalities and billions of dollars in economic losses.Accurate prediction of extreme high temperatures in this region,especially on a decadal scale,is urgently needed by governmental decision-makers to effectively address climate change and promote sustainable development.This paper assesses the decadal predictive skill of current state-of-the-art dynamical models (CMIP6 DCPP) for summer extreme high temperatures in the Eurasian mid-high latitude region.We utilize the anomaly correlation coefficient (ACC) to assess the model's skill in capturing the observed variability phase and the mean-square skill score (MSSS) as a deterministic verification metric sensitive to amplitude errors.By comparing DCPP hindcasts (initialization) with historical simulations (external forcing),we examine the sources of predictive skill.The evaluation results show that multi-model ensemble average (MME) exhibits high predictive skill for the region south of 60°N (South Eurasia,SEA),accurately forecasting its linear growth trend and prominent decadal variability during 1968—2008.However,MME shows almost no predictive skill for the decadal variability of extreme high temperatures in the North Eurasia (NEA) region,only forecasting a linear growth trend lower than observed.To improve decadal predictive skills,we developed a three-layer recurrent neural network (RNN).This model utilizes the large-sample model predictions of 86 initial fields as input,with training and testing periods of 1968—2007 and 2008—2022,respectively.Significant improvements in extreme high temperature skills in NEA and SEA during test period of 2008—2020 were observed in the RNN model.The ACC skills of NEA and SEA in RNN are 0.86 and 0.83,respectively,compared to-0.61 and-0.03 in MME.Meanwhile,the MSSSs of NEA and SEA in RNN are 0.37 and 0.52,whereas they are-1.1 and-0.94 in MME,respectively.Real-time forecasts from RNN indicate that extreme high temperatures in the SEA region will continue to rise from 2021 to 2026,with a record-breaking event in 2026.Meanwhile,the NEA region is predicted to experience anomalously fewer events in 2022,followed by fluctuating increases.A comparison of the performance of various input sizes in RNN reveals that large sample sizes are necessary for the RNN model.Additionally,incorporating additional predictors with significant physical mechanisms for extreme high-temperature events may further enhance decadal prediction skills,warranting further investigation.Nevertheless,this study provides new insights into current decadal prediction of extreme climate,offering promising scientific support for governmental decision-makers in addressing climate change.