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China is a key region for understanding fire activity and the drivers of its variability under strict fire suppression policies. Here, we present a detailed fire occurrence dataset for China, the Wildfire Atlas of China (WFAC; 2005–2018), based on continuous monitoring from multiple satellites and calibrated against field observations. We find that wildfires across China mostly occur in the winter season from January to April and those fire occurrences generally show a decreasing trend after reaching a peak in 2007. Most wildfires (84%) occur in subtropical China, with two distinct clusters in its southwestern and southeastern parts. In southeastern China, wildfires are mainly promoted by low precipitation and high diurnal temperature ranges, the combination of which dries out plant tissue and fuel. In southwestern China, wildfires are mainly promoted by warm conditions that enhance evaporation from litter and dormant plant tissues. We further find a fire occurrence dipole between southwestern and southeastern China that is modulated by the El Niño-Southern Oscillation (ENSO). Fire activity in China and its associations with climate are not well quantified at a local scale. Here, the authors present a detailed fire occurrence dataset for China and find a dipole fire pattern between southwestern and southeastern China that is modulated by the El Niño-Southern Oscillation (ENSO).

Accounting for 41.7% of China’s total land area, grasslands are linked to the livelihoods of over 20 million people. Although grassland fires cause severe damage in China every year, their spatiotemporal patterns and climate drivers are not well understood. In this study, we used grassland fire record forms provided by the National Forestry and Grassland Administration and grassland fire location data from the Wildfire Atlas of China to examine the spatiotemporal patterns and and seasonality of fires in China for the period from 2008 to 2020. We found that most grassland fires occurred in Inner Mongolia in northern China, specifically in the Hulun Buir and Xilingol grasslands. We found distinct differences in fire seasonality in northern China, which has a major fire season in April, versus southwestern China, where the major fire season occurs in February, March and April. April grassland fires in northern China are the result of strong winds, typically from the west, and spring drought. A secondary fire season in northern China occurs in October and is also driven by strong winds. The fire season in southwestern China seems to be less shaped by climatic factors such as wind speed, precipitation, and drought. This study provides support for decision-making by fire prevention and fire management authorities in China.

To better support wildfire predictions and risk assessment, multiple fire danger rating indices (FDRIs) have been developed but their credibility in China remains obscure. Compared with the satellite fire observations, 13 FDRIs are evaluated for the historical (2003–2021) forest fire frequency in China from four different time scales: active seasons, trends, interannual variations (IAVs) and discrimination of fire/non-fire days (DFDs). Most FDRIs effectively capture the double active seasons over Southwest China and the dominant active season over Northeast and South China but fail over the other regions. FDRIs with cloud cover perform better in capturing climatological fire seasonality. All FDRIs fail to reproduce the significant decreasing trend of forest fires speculatively due to local fire management and discordant changes in meteorological elements. Most FDRIs have the advantages of the IAVs and DFDs over Southwest China but exhibit deficiencies over the other regions. FDRIs incorporating wind speed perform best in representing both IAVs and DFDs, indicating the indispensable effect of surface wind on the interannual/daily variation of fire danger. This study provides a credible reference for utilizing FDRIs in China, as well as offers insights for developing better regional FDRIs to represent different time-scale variations.

The development of highly effective photosensitizers (PSs) for photodynamic therapy remains a great challenge at present. Most PSs rely on the heavy-atom effect or the spin–orbit charge-transfer intersystem crossing (SOCT-ISC) effect to promote ISC, which brings about additional cytotoxicity, and the latter is susceptible to the interference of solvent environment. Herein, an immanent universal property named photoinduced molecular vibrational torsion (PVT)-enhanced spin–orbit coupling (PVT-SOC) in PSs has been first revealed. PVT is verified to be a widespread intrinsic property of quinoid cyanine (QCy) dyes that occurs on an extremely short time scale (10–10 s) and can be captured by transient spectra. The PVT property can provide reinforced SOC as the occurrence of ISC predicted by the El Sayed rules (1ππ*–3nπ*), which ensures efficient photosensitization ability for QCy dyes. Hence, QTCy7-Ac exhibited the highest singlet oxygen yield (13-fold higher than that of TCy7) and lossless fluorescence quantum yield (ΦF) under near-infrared (NIR) irradiation. The preeminent photochemical properties accompanied by high biosecurity enable it to effectively perform photoablation in solid tumors. The revelation of this property supplies a new route for constructing high-performance PSs for achieving enhanced cancer phototherapy.

Human activities have altered disturbance patterns in many parts of world, but there is no quantitative information on patterns and trends of forest disturbance regimes in China. We applied a spectral-temporal segmentation approach over all available Landsat data to map individual disturbance patches and characterize the patterns and trends in disturbance rate, size, frequency, and severity across China’s forests. From 1986 to 2020, about 39.7% of China’s forests were disturbed with an annual rate of 1.16 ± 0.41% yr −1 . The disturbance decreased at a rate of −390 ± 142 km 2 yr −1 , primarily driven by the effective implementation of forest protection policy since 2000s. The rate, frequency, and size of disturbance generally intensified in Southeast, but weakened in Northeast China. Our high-quality, spatially explicit disturbance map provides an essential data layer to understand the landscape-scale drivers of forest dynamics and functions for important but less understood pan-temperate forest regions.

The recent increase of regional wildfire occurrences has been associated with climate change. In this study, we investigated the association between the February to March wildfire points and burned area in the southern region of China (20°N–30°N and 105°E–115°E) and the simultaneous Arctic Oscillation (AO) index during 2001–2022 and 2001–2020, respectively. After removing the El Niño-Southern Oscillation and Indian Ocean Dipole signals, time series of the regional mean fire points and burned area over the study area is significantly correlated with the AO index at − 0.37 and − 0.47, significant at the 0.1 level. Precipitation significantly affects wildfire variations. The positive AO could trigger a southeastward Rossby wave train and induce anomalous cyclone activity approximately located in the area encompassed by 15°N–27°N and 85°E–100°E. This outcome could help to enhance the southern branch trough and results in positive precipitation anomalies in southern China. This increasing moisture is conductive to reducing wildfire risks, vice versa. Our results are potentially useful for strengthening the understanding of the mechanisms of wildfire occurrences in southern China.

Wildfire in Northeastern Asia presents significant challenges to climate, ecosystems, and socio-economic systems. Here, we investigate the influence of preceding winter snow on spring wildfires in Northeastern Asia. Our results reveal soil moisture serves as a key mediator linking winter snow to spring fires. Anomalously low (high) snowfall during winter leads to drier (wetter) land surfaces in spring, thereby increasing (decreasing) spring wildfire risk. A multiple linear regression model based on this mechanistic association achieves a robust four-month lead forecast of spring burned area, with a prediction-observation Spearman correlation of 0.65 ( p  < 0.01), and explains 41% of the variability in the spring burned area across Northeast Asia. Cross-validation further confirms the significance and reliability of this lead-time relationship. Our findings highlight the pivotal role of winter snow in modulating spring wildfire activity in Northeastern Asia and demonstrate the potential to leverage snow conditions to improve seasonal wildfire forecasts.

Departures from historical wildfire regimes due to climate change have significant implications for the structure and composition of forests, as well as for fire management and operations in the Alberta region of Canada. This study analyzed the relationship between climate and wildfire and used a random forest algorithm to predict future wildfire frequencies in Alberta, Canada. Key factors driving wildfires were identified as vapor pressure deficit (VPD), sea surface temperature (SST), maximum temperature (Tmax), and the self-calibrated Palmer drought severity index (scPDSI). Projections indicate an increase in wildfire frequencies from 918 per year during 1970–1999 to 1151 per year during 2040–2069 under a moderate greenhouse gas (GHG) emission scenario (RCP 4.5) and to 1258 per year under a high GHG emission scenario (RCP 8.5). By 2070–2099, wildfire frequencies are projected to increase to 1199 per year under RCP 4.5 and to 1555 per year under RCP 8.5. The peak number of wildfires is expected to shift from May to July. These findings suggest that projected GHG emissions will substantially increase wildfire danger in Alberta by 2099, posing increasing challenges for fire suppression efforts.