Based on the observations, reanalysis and sea surface temperature (SST) data sets, the spatio‐temporal distribution characteristics of the decreasing spring persistent rainfall (SPR) over the Yangtze‐Huai River Valley (YHRV) of China during 1960–2019 are revealed, and corresponding causes of the first leading two modes from the empirical orthogonal function (EOF) analysis on the SPR are also explored. Results indicate that the SPR decreases northwards and exhibits spatially consistent declining trends over the YHRV, with the maximum along the middle and lower reaches of the Yangtze River, attributing to the decrease in total spring rainfall over the YHRV. The first EOF mode of the SPR (EOF‐1) over the YHRV shows a spatial mono‐sign pattern, while the South Indian Ocean dipole plays an important role in modulating its variability. Among them, the warming SST in the southwestern Indian Ocean is more important for the spatially consistent decrease of the SPR over the YHRV. The second EOF mode of the SPR (EOF‐2) over the YHRV exhibits a spatial distribution of north–south dipole pattern, while the North Atlantic Oscillation (NAO) contributes to the variation of EOF‐2. The inter‐decadal decreases of SPR at different rates are attributed to the transition between the first leading two EOF modes of SPR associated with the inter‐decadal changes of the external forcing factors and atmospheric circulations. From mid‐1970s to 1990s when the southern Indian Ocean SST increases rapidly, a mono‐sign pattern of the SPR is observed with a rapid decrease SPR over the whole YHRV. Since the mid‐1980s, when the NAO gradually reaches a predominantly positive phase, the SPR exhibits an increasing trend over the south of the YHRV, while a decreasing trend over the north part, causing the overall slowing of the precipitation decreasing trend.

In this study, an improved method named spatial disaggregation and detrended bias correction (SDDBC) based on spatial disaggregation and bias correction (SDBC) combined with trend correction was proposed. Using data from meteorological stations over China from 1991 to 2020 and the seasonal hindcast data from the Beijing Climate Center Climate System Model (BCC_CSM1.1 (m)), the performances of the model, SDBC, and SDDBC in spring temperature forecasts were evaluated. The results showed that the observed spring temperature exhibits a significant increasing trend in most of China, but the warming trend simulated by the model was obviously smaller. SDBC performed poorly in temperature trend correction. With SDDBC, the model’s deviation in temperature trend was corrected, and consequently, the temporal correlation between the model’s simulation and the observation as well as the forecasting skill on the phase of temperature were improved, thus improving the MSSS and the ACC. From the perspective of probabilistic prediction, the relative operating characteristic skill score (ROCSS) and the Brier skill score (BSS) of the SDDBC for three categorical forecasts were higher than those of the model and SDBC. The SDDBC’s BSS increased as the effect of the increasing resolution component was greater than that of the decreasing reliability component. Therefore, it is necessary to correct the predicted temperature trend in post-processing for the output of numerical prediction models.