The 400 mm isohyet in China serves as a critical geographical demarcation of dry and wet regions. Amidst intensifying global warming, this climatic boundary has undergone notable shifts, with significant implications for China’s agriculture, water resources, and ecosystems. This study integrates meteorological station data, the China Gridded Daily Precipitation dataset (CN05.1), and Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM IMERG) satellite observations to assess the spatiotemporal distribution of precipitation across mainland China and analyze the migration trend of the 400 mm isohyet. Furthermore, utilizing outputs from five models of the Coupled Model Intercomparison Project Phase 6 (CMIP6), we projected future trends of China’s annual mean precipitation and the 400 mm isohyet’s migration under three Shared Socioeconomic Pathways (SSPs: low, medium, and high radiative forcing scenarios) until the end of this century (2100). Results reveal that from 2001 to 2017, the 400 mm isohyet exhibited a prominent northwestward migration trend. This trend is projected to continue in the future. These findings provide a crucial reference for understanding the spatial distribution and changing dynamics of precipitation patterns in China, offering vital decision support for land resource planning and water resource management.

Detecting clouds, snow, and lakes in remote sensing images is vital due to their propensity to obscure underlying surface information and hinder data extraction. In this study, we utilize Sentinel-2 images to implement a two-stage random forest (RF) algorithm for image labeling and delve into the factors influencing neural network performance across six aspects: model architecture, encoder, learning rate adjustment strategy, loss function, input image size, and different band combinations. Our findings indicate the Feature Pyramid Network (FPN) achieved the highest MIoU of 87.14%. The multi-head self-attention mechanism was less effective compared to convolutional methods for feature extraction with small datasets. Incorporating residual connections into convolutional blocks notably enhanced performance. Additionally, employing false-color images (bands 12-3-2) yielded a 4.86% improvement in MIoU compared to true-color images (bands 4-3-2). Notably, variations in model architecture, encoder structure, and input band combination had a substantial impact on performance, with parameter variations resulting in MIoU differences exceeding 5%. These results provide a reference for high-precision segmentation of clouds, snow, and lakes and offer valuable insights for applying deep learning techniques to the high-precision extraction of information from remote sensing images, thereby advancing research in deep neural networks for semantic segmentation.