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.

For numerical weather prediction (NWP), data assimilation (DA) combines short-term forecasts and various atmospheric observations to achieve optimal initial conditions, based on which subsequent forecasts are launched. With the rapid advancements in numerical models and observing systems, DA has been significantly evolved. Modern methods now can account for uncertainties of state variables across various spatiotemporal scales, incorporate multiscale observation error statistics, and enforce dynamical constrains and model balances. Meanwhile, observations from various platforms, such as ground-based, aircraft, and satellite, have been assimilated. These include data from polar-orbiting and geostationary satellites, radar-derived radial winds and reflectivity, Global Navigation Satellite System (GNSS) radio occultations, etc. To further utilize the advanced observing systems and DA techniques for high-impact weather predictions, target observation strategies have been developed to identify areas where additional observations can yield the greatest predict improvements. Based on the advancements of DA theories and methods, China’s operational systems have made significant progress, establishing advanced operational DA systems. Over the past decade, the forecast skill of 5-day global weather prediction has improved by approximately 15%. The article reviews a century of development in DA, and discusses future directions, including the advanced DA methods, operational frameworks, integration of novel observations, and the synergy between DA and artificial intelligence.