The Qinling Mountains (QMs) are considered to be the division in geology, geochemistry, and physical geography between northern China and southern China. They have crucial effects on regional climate, especially on rainfall and temperature, and have shown great scientific relevance to climate change research in China. Using the observational daily and monthly rainfall and temperature data derived from meteorological and regional automatic stations—as well as the methods of correlation analysis, climate trend analysis, and Mann–Kendal and t tests—we revealed the spatiotemporal change characteristics of temperature and rainfall and their correlation with elevation, longitude, and latitude. The results show that the annual mean temperature (AMT) underwent a significant increasing trend in the QMs. The maximum AMT increase occurred in spring, and the minimum occurred in summer. Positive anomalies of annual mean rainfall amount (AMRA) occurred in the 1960s, 1980s, and 2010s, and negative anomalies occurred in the 1970s, 1990s, and 2000s. In the QMs, the amount of moderate rainfall (MR) occupied the maximum proportion and accounted for 27.9% of the AMRA, whereas the torrential rainfall (TR) occupied the minimum proportion and accounted for 12.8%. The AMRA amount significantly decreased by 130.1 mm from the 1980s to the 1990s and accounted for 13.5% of the measure in the 1980s. The AMT and AMRA showed consistent change trends with increases in elevation and latitude and showed the opposite trend as the longitude increased. The results offer a further understanding of the meteorological background of the QMs, helping us in further investigating the potential physical mechanisms that influence the spatiotemporal distribution characteristics of temperature and rainfall in the QMs. This study will provide a scientific basis for rainfall and temperature forecasts, with relevance to local ecosystems, agriculture, soil erosion, and the prevention and mitigation of floods in the future.

Rainfall variability and its underlying physical mechanisms are crucial for improving the predictive accuracy of July rainfall patterns in the Sichuan-Shaanxi (SS) region of Southwestern China. This study utilized observational 24 h accumulated rainfall data from China in conjunction with reanalysis products sourced from the European Centre for Medium-Range Weather Forecasts (ECMWF). The purpose of this study was to elucidate the relationship between daily variations in the daily Asian-Pacific Oscillation (APO), atmospheric circulation, and daily rainfall patterns in the SS region, and to evaluate the impact of atmospheric circulation anomalies on these relationships. The results reveal a discernible intensification in the sea–land thermal contrast associated with atmospheric circulation anomalies transitioning from the daily extremely low APO (ELA) to the extremely high APO (EHA) days. These conditions lead to an increased presence of water vapor and widespread anomalies in rainfall that exceed normal levels in the SS region. Concurrently, the increase in stations experiencing extreme rainfall events (EREs) accounts for 21.3% of the overall increase in stations experiencing rainfall. The increase in rainfall amount contributed by EREs (RA-EREs) accounts for 73.5% of the overall increase in the total rainfall amount (TRA) across the SS region. Specifically, heavy rainfall (HR) and downpour rainfall (DR) during EREs accounted for 65.7% (HR) and 95.3% (DR) of the overall increase in the TRA, respectively. Relative to the ELA days, there was a substantial 122.6% increase in the occurrence frequency of EREs and a 23.3% increase in their intensity. The study suggests that the daily APO index emerges as a better indicator of July rainfall events in the SS region, with EREs significantly contributing to the overall increase in rainfall in this region. These findings indicate the importance of improving predictive capabilities for daily variability in the APO index and their correlation with rainfall events in the SS region. The results may inform the development of effective adaptation and mitigation strategies to manage the potential impacts of EREs on agriculture, water resources, sustainable development, and infrastructure in the region.

Surface air temperature is a comprehensive function of aerosols in the atmosphere and various weather factors. However, there is no real-time aerosol concentration feedback in most operational numerical weather prediction (NWP) models. This raises a scientific question of how abnormal changes in air pollutants in a short period of time will affect the temperature prediction skill of NWP models. Thus, the study was carried out to investigate the possible influence of air pollution on the temperature forecast skill based on the operational NWP model over the Beijing–Tianjin–Hebei (BTH) region during January–February 2020. The results show that the average concentrations of PM_2.5, SO₂, NO₂ and CO over the BTH region in February were smaller than those in January by 38.5%, 35.1%, 48.0% and 33.1%, respectively. Simultaneously, the forecast skills for surface temperature in February from both regional (RMAPS, Rapid-refresh Multi-scale Analysis and Prediction System) and global (ECMWF, European Centre for Medium-Range Weather Forecasts) operational NWP models improved markedly compared with that in January. In both models, the underestimation of maximum temperature and the overestimation of minimum temperature in most cities over the BTH region in February were significantly reduced. With the 24 h (24 h) forecast lead time, the RMSE (root mean square error) of BTH daily mean, maximum and minimum temperature prediction in February based on RMAPS were 17.3%, 9.8% and 21.6% lower than that in January, respectively. These are generally consistent with the other statistical indices such as deviation and regression coefficient. As the forecast lead time extended to 48 h and 72 h forecast, the phenomena still existed and were also evident in the ECMWF model. The improvement of temperature forecast skill of NWP models may be attributed to the unexpected dramatical reduction of air pollutants. Less aerosols during the daytime allow more solar radiation reaching the surface and cause a warming in the near-surface temperature, while less aerosols during the nighttime favor the outgoing long-wave radiation and then lead to a cooling near the ground.