In this study, an improved method named spatial disaggregation and detrended bias correction (SDDBC) based on spatial disaggregation and bias correction (SDBC) combined with trend correction was proposed. Using data from meteorological stations over China from 1991 to 2020 and the seasonal hindcast data from the Beijing Climate Center Climate System Model (BCC_CSM1.1 (m)), the performances of the model, SDBC, and SDDBC in spring temperature forecasts were evaluated. The results showed that the observed spring temperature exhibits a significant increasing trend in most of China, but the warming trend simulated by the model was obviously smaller. SDBC performed poorly in temperature trend correction. With SDDBC, the model’s deviation in temperature trend was corrected, and consequently, the temporal correlation between the model’s simulation and the observation as well as the forecasting skill on the phase of temperature were improved, thus improving the MSSS and the ACC. From the perspective of probabilistic prediction, the relative operating characteristic skill score (ROCSS) and the Brier skill score (BSS) of the SDDBC for three categorical forecasts were higher than those of the model and SDBC. The SDDBC’s BSS increased as the effect of the increasing resolution component was greater than that of the decreasing reliability component. Therefore, it is necessary to correct the predicted temperature trend in post-processing for the output of numerical prediction models.

Human settlements are areas closely related to human production and life, and their quality directly affects people’s physical health and quality of life. However, with air pollution continuing to worsen, people are becoming increasingly concerned about the exposure to air pollution in their residential regions. In addition, many studies ignore the long-term impact of environmental factors on the evolution of human settlement quality (HSQ). This study aims to assess the HSQ in the Mid-Spine Belt of Beautiful China (MSBBC) from 2000 to 2020 and to analyze the changes in its driving forces during different time periods. We divided the HSQ into five dimensions: terrain conditions, climate conditions, hydrological conditions, ground cover conditions, and air pollution exposure. The Entropy-TOPSIS and grey relational degree model were used to assess the HSQ in the MSBBC. To analyze the driving forces of HSQ, the optimal-parameters-based geographic detector model was utilized. The results show the following: (1) Within the study area, the degree of the population exposure risk to PM_2.5 and its change trend were significantly different on both sides of the Hu Line, with the east showing higher levels than the west. (2) The HSQ of the MSBBC decreased from east to west. Moreover, the HSQ in the metropolitan area of the urban agglomeration was characterized from low to high. The HSQ underwent three phases, consisting of an increase (2000–2010), a decrease (2010–2015), and an increase again (2015–2020). (3) Among the driving factors, the impact of PM_2.5 on the HSQ weakened year by year. The driving force of meteorological parameters on the HSQ was the strongest. Furthermore, the interactions of each factor could enhance the HSQ. The results of this study not only provide a strong reference for creating livable areas and promoting sustainability in the MSBBC but also contribute to addressing challenges such as pollution and climate change.

Drought, which is severe and recurring in Northern China, is the most significant meteorological disaster on a worldwide scale. However, a definitive conclusion has not yet been reached on the multi-scale drought features in Northern China under a warming context. Drought is affected by several circulation-related variables and a deeper scientific comprehension of their synergistic effects is required. This research addresses the spatiotemporal aspects of summer drought and its causes in the northern drought-prone belt of China (NDPB), based on meteorological observation and data reanalysis. The primary findings indicate that the NDPB is very vulnerable to drought, with the uniform mode being the region’s most significant spatial mode associated with summer droughts. Inter-annual variations are the primary cause of the summer drought in the NDPB when considering multi-scale changes. Regional drought has typically declined during this century because of the effect of multi-decadal scale changes, which cannot be disregarded. Considering the circulation background field, during summer drought years in the NDPB, the South Asian high is weaker and moves eastward, while the high-level westerly jet is weaker and more northerly. From Lake Baikal to Northern China, there is an anomalous anticyclonic circulation, and the mid-level subtropical high in the western Pacific is more southerly and easterly. As a result, the NDPB is governed by the unusual northerly circulation. The Mongolian and northeastern surface cyclone activity is decreasing, the low-level warm and humid airflow is feeble, and the NDPB is not experiencing anomalous water vapor transport. In the high-, middle-, and low-level arrangement, the NDPB circulations diverge at low levels and converge at high levels—unusual sinking in the vertical field results in regular droughts. Multiple circulation parameters on inter-annual, inter-decadal, and multi-decadal scales interact synergistically to impact summer droughts in NDPB. However, the synergistic elements that mainly impact various timescales differ. They are primarily influenced by northerly circulation, westerly winds, and the combined effect of the South Asian summer monsoon on inter-annual timescales. The combined impact of the upper westerly winds, northerly circulation, and East Asian, South Asian, and plateau summer monsoons significantly influences their inter-decadal timescales. The westerly winds, northerly circulation, and East Asian, plateau summer monsoons are the primary multi-decadal factors.