A heat danger day is defined as an extreme when the heat stress index (a combined temperature and humidity measure) exceeding 41 °C, warranting public heat alerts. This study assesses future heat risk (i.e. heat danger days times the population at risk) based on the latest Coupled Model Intercomparison Project phase 6 projections. In recent decades (1995–2014) China’s urban agglomerations (Beijing-Tianjin-Hebei, Yangtze River Delta, Middle Yangtze River, Chongqing-Chengdu, and Pearl River Delta (PRD)) experienced no more than three heat danger days per year, but this number is projected to increase to 3–13 days during the population explosion period (2041–2060) under the high-emission shared socioeconomic pathways (SSP3-7.0 and SSP5-8.5). This increase will result in approximately 260 million people in these agglomerations facing more than three heat danger days annually, accounting for 19% of the total population of China, and will double the current level of overall heat risk. During the period 2081–2100, there will be 8–67 heat danger days per year, 60%–90% of the urban agglomerations will exceed the current baseline number, and nearly 310 million people (39% of the total China population) will be exposed to the danger, with the overall heat risk exceeding 18 times the present level. The greatest risk is projected in the PRD region with 67 heat danger days to occur annually under SSP5-8.5. With 65 million people (68% of the total population) experiencing increased heat danger days, the overall heat risk in the region will swell by a factor of 50. Conversely, under the low-emission pathways (SSP1-2.6 and SSP2-4.5), the annual heat danger days will remain similar to the present level or increase slightly. The result indicates the need to develop strategic plans to avoid the increased heat risk of urban agglomerations under high emission-population pathways.

Using non-accidental mortality records from 195 sites and 11 model simulations from the Coupled Model Intercomparison Project phase 6, we assessed future avoidable heat-related deaths associated with emission mitigation after accounting for population changes in China. Without human-induced climate change in the 21st century, China’s heat-related mortality would drop by 48–72% (95% confidence interval (CI): 40–76%), especially at high latitudes and altitudes (up to 80%). Nationally, 15,576–87,612 (95% CI: 6605–106,736) heat-related deaths per year would potentially be avoided by reducing emissions. For most of China, temperature changes dominate the variation in heat-related deaths, while in Central and South China, population changes will have a greater influence. These not only reinforce the necessity of mitigating emissions but also suggest that adjusting population structure and spatial distribution could be applied in some areas to minimize warming impacts on public health. Notably, given that abatement will hardly bring immediate success within the next twenty years, more healthcare infrastructures are urgently needed to manage potential growing heat risks. Particularly in Northwest and South China, where future heat-related deaths would still be 110–140% of current levels (1995–2014) even without anthropogenic warming. We also found that without historical human-induced emissions, China’s low level of technology (medical and economic) might increase current heat-related mortality by 57%, even though warming could be mitigated. Consequently, we believe that mitigating climate change risks also requires consideration of the economic/medical losses from lowering emissions.

Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. The International Best Track Archive for Climate Stewardship (IBTrACS) dataset provides widely used data to estimate TC climatology. However, it has low data coverage, lacking intensity and outer-size data for more than half of all recorded storms, and is therefore insufficient as a reference for researchers and decision makers. To fill this data gap, we reconstruct a long-term TC dataset by integrating IBTrACS and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data. This reconstructed dataset covers the period 1959–2022, with 3 h temporal resolution. Compared to the IBTrACS dataset, it contains approximately 3–4 times more data points per characteristic. We establish machine learning models to estimate the maximum sustained wind speed (V_max) and radius of maximum wind (R_max) in six basins for which TCs are generated, using ERA5-derived 10 m azimuthal mean azimuthal wind profiles as input, with V_max and R_max data from the IBTrACS dataset used as learning target data. Furthermore, we employ an empirical wind–pressure relationship and six wind profile models to estimate the minimum central pressure (P_min) and outer size of the TCs, respectively. Overall, this high-resolution TC reconstruction dataset demonstrates global consistency with observations, exhibiting mean biases of <1 % for V_max and 3 % for R_max and P_min in almost all basins. The dataset is publicly available from https://doi.org/10.5281/zenodo.13919874 (Xu et al., 2024) and substantially advances our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters.

Using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, we tracked the paths of 46 autumn cold surges affecting North China from 1961 to 2014, and classified them by clustering analysis, thereby investigating their changes and associated atmospheric circulation evolution. Our results indicate that autumn cold surges affecting North China can be classified into three types according to their paths: the north type, west type, and northwest type, with occurrences of 12, 16, and 18 respectively. Different types of cold surges have different atmospheric circulation characteristics. The north type is associated with a blocking type of atmospheric circulation pattern, with an enhanced stretching northeast ridge over the Ural Mountains and a transverse trough over Lake Baikal. However, the northwest type is characterized by a ridge–trough–ridge wave-train pattern that is located over the Barents Sea, West Siberian Plain, and Sakhalin Island, respectively. The west-type cold surge is related to a conversion type: a blocking system over the Ural Mountains forms four days before the cold surge occurrence, after which it becomes a wave-train type. The atmospheric signals detected prior to the occurrences of the three types of cold surges are also explored. The main signal of the north-path cold surges is that the energy propagates eastward from the Azores Islands to the Ural Mountains, and then forms a blocking high over the Urals. However, for the northwest-path cold surges, there is a weak trough over the Ural Mountains that gradually strengthens because the blocking high collapses over the Norwegian Sea. The key signal of the formation of the west-path cold surges is a blocking high over the Norwegian Sea’s continuous enhancement and extension to Novaya Zemlya, which results in a transmission of energy to the Ural Mountains and leads to the formation of a blocking system over here. When the above-mentioned different types of atmospheric circulation characteristics appear, the type of cold surge path and its impact area can be potentially forecasted in advance, which may reduce the losses that result from cold surges.