Background: Brucellosis is a common zoonotic infectious disease in China. This study aimed to investigate the incidence trends of brucellosis in China, construct an optimal prediction model, and analyze the driving role of climatic factors for human brucellosis. Methods: Using brucellosis incidence, and the socioeconomic and climatic data for 2014–2020 in China, we performed spatiotemporal analyses and calculated correlations with brucellosis incidence in China, developed and compared a series of regression and Seasonal Autoregressive Integrated Moving Average X (SARIMAX) models for brucellosis prediction based on socioeconomic and climatic data, and analyzed the relationship between extreme weather conditions and brucellosis incidence using copula models. Results: In total, 327,456 brucellosis cases were reported in China in 2014–2020 (monthly average of 3898 cases). The incidence of brucellosis was distinctly seasonal, with a high incidence in spring and summer and an average annual peak in May. The incidence rate was highest in the northern regions’ arid and continental climatic zones (1.88 and 0.47 per million people, respectively) and lowest in the tropics (0.003 per million people). The incidence of brucellosis showed opposite trends of decrease and increase in northern and southern China, respectively, with an overall severe epidemic in northern China. Most regression models using socioeconomic and climatic data cannot predict brucellosis incidence. The SARIMAX model was suitable for brucellosis prediction. There were significant negative correlations between the proportion of extreme weather values for both high sunshine and high humidity and the incidence of brucellosis as follows: high sunshine, $$r$$r = −0.59 and −0.69 in arid and temperate zones; high humidity, $$r$$r = −0.62, −0.64, and −0.65 in arid, temperate, and tropical zones. Conclusions: Significant seasonal and climatic zone differences were observed for brucellosis incidence in China. Sunlight, humidity, and wind speed significantly influenced brucellosis. The SARIMAX model performed better for brucellosis prediction than did the regression model. Notably, high sunshine and humidity values in extreme weather conditions negatively affect brucellosis. Brucellosis should be managed according to the “One Health” concept.

Hot and dry extremes have co-occurred with global warming, causing worse threats than single climate extremes. We analyze the compound hot and dry day (CHDD) and population exposure (E_pop) across China based on observations and 11 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6. The bias-corrected GCMs, particularly the two-stage multivariate quantile mapping-corrected ones, perform better than the raw ones in catching changes in CHDD but they all overestimate CHDD and E_pop. More CHDDs occur in the southeast (SE), north (NC) and northwest (NW) China with increasing trends over regions stretching from the northeast (NE) to southwest (SW) China. Despite the declined CHDD, historical E_pop has generally increased by the increased population. Future projections in the SSP5-8.5 experiment indicate an overall increase of CHDD and E_pop at 1.5°C warming and an increasing E_pop to dangerous CHDD at 2°C warming. SW, NE and the Tibetan Plateau need effective measures of adaptation to reduce sufferings from CHDD under climate change.

The long‐term variability of total, direct and diffuse solar radiation across China during 1958–2016 is investigated based on a ground‐measured daily radiation dataset. Missing data are estimated using a 3‐day average moving window and a backpropagation artificial neural network (BP network). The BP network achieves better estimates of direct (R² = 0.32–0.96) than diffuse radiation (R² = 0.00–0.81). A dimming period during 1958–1990 and a “From Dimming to Brightening” transition between 1990 and 1993 have been detected across China. The declining ratio of direct to diffuse radiation suggests a degrading air quality caused by increasing aerosols in eastern China. To study the aerosol effect on radiation, two empirical models are developed from 2000–2016 using the ground‐measured total radiation, sunshine duration and satellite‐retrieved total aerosol concentration. Both models perform well in the estimate of direct (R² = 0.71–0.89) and diffuse radiation (R² = 0.63–0.95). The increasing total radiation in eastern China since 2000 is mainly contributed by diffuse radiation. Besides, small anthropogenic aerosols can increase diffuse fraction, the proportion of diffuse in total radiation, whereas large natural aerosols may reduce it. The BP network and empirical models exhibit a better agreement in the estimate of direct than diffuse radiation in eastern China, which highlights the impact of aerosols on diffuse radiation in the recent decade.

Terrestrial water storage (TWS) is a critical variable in the global hydrological cycle. The TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) allow us to better understand water exchanges between the atmosphere, land surface, sea, and glaciers. However, missing historical (pre-2002) GRACE data limit their further application. In this study, we developed a random forest (RF) model to reconstruct the monthly terrestrial water storage anomaly (TWSA) time series using Global Land Data Assimilation System (GLDAS) and Climatic Research Unit (CRU) data for the Lancang-Mekong River basin. The results show that the RF-built TWSA time series agrees well with the GRACE TWSA time series for 2003–2014, showing that correlation coefficients (R) of 0.97 and 0.90 at the basin and grid scales, respectively, which demonstrates the reliability of the RF model. Furthermore, this method is used to reconstruct the historical TWSA time series for 1980–2002. Moreover, the discharge can be obtained by subtracting the evapotranspiration (ET) and RF-built terrestrial water storage change (TWSC) from the precipitation. The comparison between the discharge calculated from the water balance method and the observed discharge showed significant consistency, with a correlation coefficient of 0.89 for 2003–2014 but a slightly lower correlation coefficient (0.86) for 1980–2002. The methods and findings in this study can provide an effective means of reconstructing the TWSA and discharge time series in basins with sparse hydrological data.