Anthropogenic warming is a key climate indicator of rising urban fire activity in China.

China, one of the most populous countries in the world, has suffered the highest number of natural disaster-related deaths from fire. On local scales, the main causes of urban fires are anthropogenic in nature. Yet, on regional to national scales, little is known about the indicators of large-scale co-varying urban fire activity in China. Here, we present the China Fire History Atlas (CFHA), which is based on 19 947 documentary records and represents fires in urban areas of China over the twentieth century (1901-1994). We found that temperature variability is a key indicator of urban fire activity in China, with warmer temperatures being correlated with more urban fires, and that this fire-temperature relationship is seasonally and regionally explicit. In the early twentieth century, however, the fire-temperature relationship was overruled by war-related fires in large urban areas. We further used the fire-temperature relationship and multiple emissions scenarios to project fire activity across China into the twenty-first century. Our projections show a distinct increase in future urban fire activity and fire-related economic loss. Our findings provide insights into fire-climate relationships in China for densely-populated areas and on policy-relevant time scales and they contribute spatial coverage to efforts to improve global fire models.

Cenozoic Indo-Pacific warm pool controlled by both atmospheric CO2 and paleogeography.

The Indo-Pacific warm pool (IPWP) is crucial for regional and global climates. However, the development of the IPWP and its effect on the regional climate during the Cenozoic remain unclear. Here, using a compilation of sea surface temperature (SST) records (mainly since the middle Miocene) and multimodel paleoclimate simulations, our results indicated that the extent, intensity and warmest temperature position of the IPWP changed markedly during the Cenozoic. Specifically, its extent decreased, its intensity weakened, and its warmest temperature position shifted from the Indian to western Pacific Ocean over time. The atmospheric CO2 dominated its extent and intensity, while paleogeography, by restricting the distribution of the Indian Ocean and the width of the tropical seaways, controlled the shift in its warmest temperature position. In particular, the eastward shift to the western Pacific Ocean from the middle to late Miocene inferred from compiled SST records likely resulted from the constriction of tropical seaways. Furthermore, by changing the atmospheric thermal structure and atmospheric circulation, the reduced extent and intensity of the IPWP decreased the annual precipitation in the western Indian Ocean, eastern Asia and Australia, while the shift in the warmest temperature position from the Indian to western Pacific Ocean promoted aridification in Australia. Qualitative model-data agreements are obtained for both the IPWP SST and regional climate. From the perspective of past warm climates with high concentrations of atmospheric CO2, the expansion and strengthening of the IPWP will occur in a warmer future and favor excessive precipitation in eastern Asia and Australia.

How skillful was the projected temperature over China during 2002-2018?

Climate change has attracted significant attention due to its increasing impacts on various aspects of the world, and future climate projections are of vital importance for associated adaptation and mitigation, particularly at the regional scale. However, the skill level of the model projections over China in the past more than ten years remains unknown. In this study, we retrospectively investigate the skill of climate models within the Third (TAR), Fourth (AR4), and Fifth (AR5) Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC) for the near-term projections of near-surface (2 m) air temperature changes in China. Those models are revealed to be skillful in projecting the subsequent climatology and trend of the temperature changes in China during 2002-2018 from several to ten years ahead, with higher scores for the climatology than for the trend. The model projections display cold biases against observations in most of China, while the nationally averaged trend is overestimated by TAR models during 2002-2018 but underestimated by AR4 models during 2008-2018. For all emission scenarios, there is no obvious difference between the equal- and unequal-weighted averages based on the arithmetic averaging and reliability ensemble averaging method respectively, however the uncertainty range of projection is narrowed after weighting. The near-term temperature projections differ slightly among various emission scenarios for the climatology but are largely different for the trend.

Future extreme climate changes linked to global warming intensity.

Based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) daily dataset, we investigate changes of the terrestrial extreme climates given that the global mean temperature increases persistently under the Representative Concentration Pathways 8.5 (RCP8.5) scenario. Compared to preindustrial conditions, more statistically significant extreme temperatures, precipitations, and dry spells are expected in the 21st century. Cold extremes decrease and warm extremes increase in a warmer world, and cold extremes tend to be more sensitive to global warming than the warm ones. When the global mean temperature increases, cold nights, cold days, and warm nights all display nonlinear relationships with it, such as the weakening of the link projected after 3 °C global warming, while the other indices generally exhibit differently, with linear relationships. Additionally, the relative changes in the indices related to extreme precipitation show significantly consistent linear changes with the global warming magnitude. Compared with the precipitation extremes, changes in temperature extremes are more strongly related to the global mean temperature changes. For the projection of the extreme precipitation changes, models show higher uncertainty than that in extreme temperature changes, and the uncertainty for the precipitation extremes becomes more remarkable when the global warming exceeds 5 °C.