Carbendazim, a widely used fungicide in China, has been found to have reproductive toxicity and mutagenic effects. However, information on the spatial-temporal variations of carbendazim residues in food in China is limited. Here, we investigated the presence of carbendazim in China's plant-based foods from 2011 to 2020, evaluated the spatial-temporal characteristics, and assessed the associated exposure risks in the Chinese diet. The results revealed a high detection frequency of carbendazim in fruits (26.4%) and high concentrations in vegetables (∼110 mg kg⁻¹), indicating widespread misuse of the fungicide. The acute risks of consuming certain vegetables and cereals exceeded the recommended limits by up to 12 and 5 times, respectively. Although there has been a decline in carbendazim residue levels in food since the implementation of the Chinese government's action plan for zero growth of pesticide use in 2015, some provinces still exhibited high levels of carbendazim in multiple food categories, which were positively correlated with annual pesticide application. We highlight that carbendazim contamination reflects the broader issue of pesticide use in China. It emphasizes the need for committed and targeted national policies to reduce carbendazim residues in food and suggests that such measures could also regulate the use of other pesticides, given that pesticide abuse in China is not limited to specific types. We call for the re-evaluation of maximum residue limits of carbendazim, particularly in highly consumed foods such as cereals.

Fire forecasts that predict dry‐season fire activities several months in advance are beneficial for fire management. On a global scale, however, the predictability of fires is limited because fires depend on multiple factors and lack a single dominant predictor to describe diverse fire characteristics across regions. Here, based on 33 local meteorological parameters (MPs) and 37 large‐scale climate indices (CIs), we establish four empirical model clusters to predict global interannual fire variability. We show that across various geographic locations, the models provide reliable fire forecasts at least three months prior to the peak fire months. Compared to MPs, CIs such as the Oceanic Niño Index are comparable or even superior predictors. Globally, as well as in most continents, the El Niño–Southern Oscillation is the major driving force, explaining 17% of interannual fire variability, with strong implications for fire carbon emissions and the global carbon cycle. Other important predictors include the Northern Atlantic sea surface temperature (9%), the Southern Atlantic sea surface temperature (5%), and the Pacific/North American Pattern (3%). The predictive models reveal a strong interaction between MPs and CIs, indicating potential climate‐induced modification of fire responses to meteorological conditions. We show that the newly developed predictive models can benefit future fire management in response to climate change.