Based on the observational dataset CN05.1 and the Coupled Model Intercomparison Project (CMIP), this study assesses the performance of CMIP5 and CMIP6 projects in projecting mean precipitation at annual and seasonal timescales in the Yangtze River Basin of China over the period 2015–2020 under medium emission scenarios (RCP4.5/SSP2-4.5). Results indicate that the multi-model ensemble (MME) of CMIP6 overall has lower relative bias and root-mean square error of both annual and seasonal mean than that of CMIP5, except for winter, but both of the two ensembles show the best projected accuracy in winter. Generally, CMIP6 outperformed CMIP5 in capturing spatial and temporal pattern over the YRB, especially in the midstream and downstream areas, which have high precipitation. Further analyses suggest that the CMIP6 GCMs have lower median normalized root-mean square error than CMIP5 GCMs. Based on the Taylor skill (TS) score, both CMIP6 and CMIP5 GCMs are ranked to evaluate relative model performance. CMIP6 GCMs have higher ranks than CMIP5 GCMs, with an average TS score of 0.68 (0.55) for CMIP6 (CMIP5), and three out of the five highest scored GCMs are CMIP6 GCMs. However, the CMIP6 precipitation projections are still quite uncertain, thus requiring further assessment and correction.

The urban wind environment is an important element of urban microclimates and plays an important role in the quality of the urban environment. The computational fluid dynamics (CFD) simulation method is an important means for urban wind field research. However, CFD simulation has high requirements for computer hardware and software. In this paper, based on geographic information system (GIS) technology, a new building data generalization method was developed to solve the problems of a huge amount of data and calculations in urban-scale CFD wind field simulations. Using Fluent software and high-precision urban building geographic information data with elevation attributes, the method was applied to Jinjiang City, Fujian Province, China. A CFD simulation of the wind field of Jinjiang City was implemented, and detailed, intuitive wind field information was obtained, which were compared with the measured data. The results show that the building data generalization method could effectively improve the efficiency of the city's overall wind field CFD simulation. The simulated wind speed was significantly correlated with the measured data, but it was overestimated. The simulated wind direction was consistent with the measured data of most stations. The simulation results were reasonable and could provide reference for application and subsequent research.

Terrain plays an important role in the formation, development and distribution of local precipitation and is a major factor leading to locally abnormal weather in weather systems. Although small-scale topography has little influence on the spatial distribution of precipitation, it interferes with precipitation fitting. Due to the arbitrary combination of small, medium and large-scale terrain, complex terrain distribution is formed, and small-scale terrain cannot be clearly defined and removed. Based on the idea of bidimensional empirical mode decomposition (BEMD), this paper extracts small-scale terrain data layer by layer to smooth the terrain and constructs a macroterrain model for different scales in Central China. Based on the precipitation distribution model using multiple regression, precipitation models (B0, B1, B2 and B3) of different scales are constructed. The 18-year monthly average precipitation data of each station are compared with the precipitation simulation results under different scales of terrain and TRMM precipitation data, and the influence of different levels of small-scale terrain on the precipitation distribution is analysed. The results show that (1) in Central China, the accuracy of model B2 is much higher than that of TRMM model A and monthly precipitation model B0. The comprehensive evaluation indexes are increased by 3.31% and 1.92%, respectively. (2) The influence of different levels of small-scale terrain on the precipitation distribution is different. The first- and second-order small-scale terrain has interference effects on precipitation fitting, and the third-order small-scale terrain has an enhancement effect on precipitation. However, the effect of small-scale topography on the precipitation distribution is generally reflected as interference.

Tea is a special economic crop that is widely distributed in tropical and subtropical areas. Timely and accurate access to the distribution of tea plantation areas is crucial for effective tea plantation supervision and sustainable agricultural development. Traditional methods for tea plantation extraction are highly dependent on feature engineering, which requires expensive human and material resources, and it is sometimes even difficult to achieve the expected results in terms of accuracy and robustness. To alleviate such problems, we took Xinchang County as the study area and proposed a method to extract tea plantations based on deep learning networks. Convolutional neural network (CNN) and recurrent neural network (RNN) modules were combined to build an R-CNN model that can automatically obtain both spatial and temporal information from multitemporal Sentinel-2 remote sensing images of tea plantations, and then the spatial distribution of tea plantations was predicted. To confirm the effectiveness of our method, support vector machine (SVM), random forest (RF), CNN, and RNN methods were used for comparative experiments. The results show that the R-CNN method has great potential in the tea plantation extraction task, with an F1 score and IoU of 0.885 and 0.793 on the test dataset, respectively. The overall classification accuracy and kappa coefficient for the whole region are 0.953 and 0.904, respectively, indicating that this method possesses higher extraction accuracy than the other four methods. In addition, we found that the distribution index of tea plantations in mountainous areas with gentle slopes is the highest in Xinchang County. This study can provide a reference basis for the fine mapping of tea plantation distributions.

The shifts in dry-wet climate regions are a natural response to climate change and have a profound impact on the regional agriculture and ecosystems. In this paper, we divided China into four dry-wet climate regions, i.e., arid, semi-arid, semi-humid, and humid regions, based on the humidity index (HI). A comparison of the two 30-year periods, i.e., 1960–1989 and 1990–2019, revealed that there was a shift in climate type in each dry-wet climate region, with six newly formed transitions, and the total area of the shifts to wetter conditions was more than two times larger than that of the shifts to drier conditions. Interestingly, the shifts to drier types were basically distributed in the monsoon region (east of 100${}^{\circ }$ E) and especially concentrated in the North China Plain where agricultural development relies heavily on irrigation, which would increase the challenges in dealing with water shortage and food production security under a warming climate. The transitions to wetter types were mainly distributed in western China (west of 100${}^{\circ }$ E), and most areas of the Junggar Basin have changed from arid to semi-arid region, which should benefit the local agricultural production and ecological environment to some extent. Based on a contribution analysis method, we further quantified the impacts of each climate factor on HI changes. Our results demonstrated that the dominant factor controlling HI changes in the six newly formed transition regions was P, followed by air temperature (Ta). In the non-transition zones of the arid and semi-arid regions, an increase in P dominated the increase of HI. However, in the non-transition zones of the semi-humid and humid region with a more humid background climate, the thermal factors (e.g., Ta, and net radiation (Rn)) contributed more than or equivalent to the contribution of P to HI change. These findings can provide scientific reference for water resources management and sustainable agricultural development in the context of climate change.

The application of a deep learning algorithm (DL) can more accurately predict the initial flowering period of Platycladus orientalis (L.) Franco. In this research, we applied DL to establish a nationwide long-term prediction model of the initial flowering period of P. orientalis and analyzed the contribution rate of meteorological factors via Shapely Additive Explanation (SHAP). Based on the daily meteorological data of major meteorological stations in China from 1963–2015 and the observation of initial flowering data from 23 phenological stations, we established prediction models by using recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R²) were used as training effect indicators to evaluate the prediction accuracy. The simulation results show that the three models are applicable to the prediction of the initial flowering of P. orientalis nationwide in China, with the average accuracy of the GRU being the highest, followed by LSTM and the RNN, which is significantly higher than the prediction accuracy of the regression model based on accumulated air temperature. In the interpretability analysis, the factor contribution rates of the three models are similar, the 46 temperature type factors have the highest contribution rate with 58.6% of temperature factors’ contribution rate being higher than 0 and average contribution rate being 5.48 × 10⁻⁴, and the stability of the contribution rate of the factors related to the daily minimum temperature factor has obvious fluctuations with an average standard deviation of 8.57 × 10⁻³, which might be related to the plants being sensitive to low temperature stress. The GRU model can accurately predict the change rule of the initial flowering, with an average accuracy greater than 98%, and the simulation effect is the best, indicating that the potential application of the GRU model is the prediction of initial flowering.