It is important to project the changes in extreme temperature in Eurasia, where more than two‐thirds of the world's population reside. Employing Phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations and extreme temperature indices defined by the expert team on climate change detection and Indices, we firstly evaluate the performance of the CMIP6 models, and then project the spatial patterns of changes in extreme temperature in different periods under shared social‐economic Pathway scenarios and at different global warming levels. The results show that the performance of the CMIP6 models in simulating the indices of the coldest day (TXn), the coldest night (TNn), summer days (SU), tropical nights (TR) and frost days (FD) are good. Therefore, these five indices were selected for projection. Overall, TXn, TNn, SU and TR show an increasing trend and FD a decreasing trend, consistent with global warming in the future. The responses to global warming tend to be strongest in high latitudes for TXn and TNn, in high latitudes and high‐altitude areas for FD, and in some low‐latitude areas for SU and TR. At the local scale over Eurasia, where the change is larger than the regional median level, the changes in extreme temperature indices at 1.5°C of global warming above pre‐industrial levels are projected to be reduced by 30–55% and 55–85%, respectively, compared with the situation at 2.0°C and 3.0 warming. If global warming could be controlled to within 2.0°C, the changes in extreme temperature indices over Eurasia would be reduced by up to 60% compared with the situation at 3.0°C warming. Therefore, if global warming can be controlled to within a low warming target, the risk of extreme temperature change will be greatly reduced in these regions.

Ground-based microwave radiometers (MWRPS) can provide continuous atmospheric temperature and relative humidity profiles for a weather prediction model. We investigated the impact of assimilation of ground-based microwave radiometers based on the rapid-refresh multiscale analysis and prediction system-short term (RMAPS-ST). In this study, five MWRP-retrieved profiles were assimilated for the precipitation enhancement that occurred in Beijing on 21 May 2020. To evaluate the influence of their assimilation, two experiments with and without the MWRPS assimilation were set. Compared to the control experiment, which only assimilated conventional observations and radar data, the MWRPS experiment, which assimilated conventional observations, the ground-based microwave radiometer profiles and the radar data, had a positive impact on the forecasts of the RMAPS-ST. The results show that in comparison with the control test, the MWRPS experiment reproduced the heat island phenomenon in the observation better. The MWRPS assimilation reduced the bias and RMSE of two-meter temperature and two-meter specific humidity forecasting in the 0–12 h of the forecast range. Furthermore, assimilating the MWRPS improved both the distribution and the intensity of the hourly rainfall forecast, as compared with that of the control experiment, with observations that predicted the process of the precipitation enhancement in the urban area of Beijing.

In this study, the temperature and relative humidity profiles retrieved from five ground-based microwave radiometers in Beijing were assimilated into the rapid-refresh multi-scale analysis and prediction system-short term (RMAPS-ST). The precipitation bifurcation prediction that occurred in Beijing on 4 May 2019 was selected as a case to evaluate the impact of their assimilation. For this purpose, two experiments were set. The Control experiment only assimilated conventional observations and radar data, while the microwave radiometers profilers (MWRPS) experiment assimilated conventional observations, the ground-based microwave radiometer profiles and radar data into the RMAPS-ST model. The results show that in comparison with the Control test, the MWRPS test made reasonable adjustments for the thermal conditions in time, better reproducing the weak heat island phenomenon in the observation prior to the rainfall. Thus, assimilating MWRPS improved the skills of the precipitation forecast in both the distribution and the intensity of rainfall precipitation, capable of predicting the process of belt-shaped radar echo splitting and the precipitation bifurcation in the urban area of Beijing. The assimilation of the ground-based microwave radiometer profiles improved the skills of the quantitative precipitation forecast to a certain extent. Among multiple cycle experiments, the onset of 0600 UTC cycle closest to the beginning of rainfall performed best by assimilating the ground-based microwave radiometer profiles.