In recent years, extreme weather and climate events, especially the precipitation extremes have received more and more attention because these events have tremendous societal impacts (

Using the NCEP/NCAR reanalysis and station observations in China during 1979–2018, features of propagation of baroclinic wave packets in the upper troposphere and their relationships with precipitation over the middle and lower reaches of the Yangtze River (MLRYR) in daily climatology are investigated after having components with periods longer than 9 days filtered out. It is demonstrated that in the filtered daily climatology, the baroclinic wave packets still exist. The wave packet migrates eastward on the northern sides of the westerly jet stream axis whereas it does not move explicitly in the southern side of the axis in the upper troposphere. The filtered daily precipitation over MLRYR is mainly affected by the wave packets in the region south of the westerly jet axis. These results are very meaningful for better understanding behaviors of Rossby waves in daily climatology and the causes of daily precipitation variations in MLRYR.

The characteristics and related mechanisms of the interannual variability of late summer (August) extreme precipitation in West China (WC) were investigated from 1961 to 2021. Precipitation and extreme precipitation (defined as the 99th percentile) generally decreased in the southeast-northwest direction, with a maximum in the Sichuan Basin. The non-linear trends in extreme precipitation have increased since the 1980s. Therefore, we further found that the interannual increase in extreme precipitation in the WC was significantly related to the eastward-strengthened South Asian high, western-stretched Western Pacific Subtropical high, enhanced westerly jet, anomalous cyclone in Mongolia, and anomalous anticyclone in the western Pacific. The anti-cyclonic anomaly is a Gill-type response to increase the sea surface temperature in the western Pacific. A mid-high latitude barotropic Rossby-wave train can be induced and has essential effects on the above key circulation patterns, further cooperating with the strong updrafts rather than strengthening and maintaining extreme precipitation in the WC.

Using ERA5 reanalysis data from March 2021 to February 2022 and the China Meteorological Administration Global Forecasting System (CMA‐GFS) operational forecast dataset of 500 hPa geopotential height in the Northern Hemisphere in the same period, the multiscale features of forecast errors are analyzed. The results indicate that the anomaly correlation coefficient (ACC) of 500 hPa geopotential height and its multiscale components in the Northern Hemisphere keep decreasing with the extension of forecast lead time, and there are no seasonal differences in the evolution of the ACC. The effective forecast skills by season for the CMA‐GFS model are above 6 days at multiscale, with the highest skills in winter and the planetary‐scale components. In space, significant seasonal differences are observed in the locations of the extreme values of multiscale forecast errors for 500 hPa geopotential height, and the spatial distribution of forecast errors reflects the inadequate prediction of the intensity of large‐scale trough and ridge systems at middle and high latitudes and the phase‐shift prediction of small troughs and ridges at middle latitudes. Generally, the forecast errors of the original field and planetary‐scale component show wavelike or banded distribution, and the synoptic‐scale forecast errors are always distributed in latitudinal wavelike patterns alternating between positive and negative, without significant differences in the distribution of land, sea, and terrain. The first empirical orthogonal function modes of multiscale forecast errors almost retain their respective feature. In temporal, the spring, summer, and autumn time series all have quasi‐biweekly positive and negative phase transitions within the monthly scale, and the significant phase transition in winter only occurs around January 1st. These results deepen the understanding of the distribution and possible causes of forecast errors of the CMA‐GFS model and provide ideas for the improvement and revision of the model.