Atmospheric reanalysis data play a critical role in a wide range of meteorological applications, such as weather and climate studies, assessment of meteorological resources, and artificial intelligence-driven forecasting. The China Meteorological Administration (CMA) Regional Re-Analysis version 1.0 (CMA-RRA v1.0) system, developed using the CMA mesoscale (CMA-MESO) model and its three-dimensional variational (3DVAR) data assimilation system, aims to generate long-term, high-resolution 3D atmospheric fields. This paper describes the processing framework of the CMA-RRA v1.0 system and reports on the development of a regional reanalysis dataset (2010–2024) produced by the system. The dataset covers the entire China region with a 3-km horizontal resolution, 70 sigma levels, and a 1-h temporal resolution. High-density local observations are assimilated during the reanalysis process, including automatic surface weather observations, reflectivity and radial velocity data from the CMA weather radar network, and data from Fengyun series satellites. Compared with observations and the global ECMWF reanalysis version 5 (ERA5), CMA-RRA v1.0 demonstrates superior performance. It shows closer agreement with radiosonde observations and yields higher equitable threat scores for precipitation at all intensity levels. Furthermore, the probability density distribution of CMA-RRA v1.0 precipitation aligns more closely with observations. Owing to its high resolution and assimilation of denser local data, CMA-RRA v1.0 also accurately reproduces historical high-impact weather events, such as the 3–4 May 2017 squall line, Typhoon Lekima (2019), and the July 2023 extreme rainfall in North China.

Since the launch of China’s Fengyun-3 (FY-3) satellite series in 2008, the on-board Microwave Temperature Sounders (MWTS) have provided critical atmospheric sounding data for numerical weather prediction and extreme weather monitoring. However, their application in climate research remains limited. To establish long-term Fundamental Climate Data Records (FCDRs) for FY-3 satellites—the core foundation of climate research—a comprehensive assessment of data consistency between China’s FY-3 satellites and the U.S. NOAA satellites is essential. This study systematically evaluates the inter-satellite consistency between FY-3 MWTS observations and three datasets of NOAA FCDRs, focusing on comparison of brightness temperatures and their anomalies over the extended period of 2009–2024. In accordance with the Global Climate Observing System (GCOS) Essential Climate Variables (ECVs) requirements (GCOS-245), the accuracy and stability of both operational and recalibrated FY-3 brightness temperatures are quantified on a global grid scale, using multiple statistical metrics including root mean square error (RMSE), standard deviation (SD), bias, and linear trend. The key findings are as follows. (1) FY-3 MWTS observations can effectively capture the characteristic seasonal cycles and vertical atmospheric structures of brightness temperatures, confirming their basic reliability in climate-related analyses. (2) Significant discontinuities in brightness temperatures are observed in the upper troposphere and lower stratosphere for earlier FY-3 satellites (FY-3A/3B/3C), indicating limitations in their long-term climate consistency, while newer satellites (FY-3D/3E/3F) show substantially improved consistency with NOAA FCDRs. (3) The recalibrated data of FY-3D exhibit a marked quality improvement, with RMSE reduced by 60% compared to FY-3D operational observations, and this recalibrated FY-3D dataset is recommended as the preferred choice for climate applications. (4) Even the operational (non-recalibrated) brightness temperature data of FY-3D achieves remarkable consistency with global benchmarks, boasting a global mean accuracy of 0.270 ± 0.039 K, which fully meets the accuracy thresholds specified by GCOS for ECVs. (5) For specific MWTS channels, the global mean brightness temperatures of Channel 4 (over oceans), Channel 6, and Channel 9 generally meet the stability requirements for climate monitoring, further supporting their utility in long-term climate studies. (6) Larger RMSEs of FY-3D operational data are identified in two key regions: the stratosphere over high latitudes and the mid troposphere over topographically complex areas (e.g., the Qinghai–Xizang Plateau, the Andes Mountains, and parts of Africa). These discrepancies are attributed to two technical factors: non-linearity in the MWTS calibration process and orbital drift of the FY-3 satellites. This study validates the climate monitoring capabilities of FY-3 MWTS observations, clarifies key directions for data quality improvement, and lays an important foundation for the transformation of this data from product generation to climate applications.