New statistical prediction scheme for monthly precipitation variability in the rainy season over northeastern China

Abstract

This study focused on seasonal prediction for monthly precipitation over Northeast China (NEC) in the rainy season. A statistical method combining empirical orthogonal function (EOF) decomposition and multi‐linear regression was developed and tested. For each EOF mode of each month in summer, the relationship between the EOF and SSTs in the previous winter was investigated and indices were constructed to be used as predictors. The predictors were required to be physically connected to the predictand and to perform well in cross validation. Monthly rainfall was reconstructed from the predicted time series and the observed spatial load of the first three EOF modes. The results of the leave‐one‐out cross‐validation for 1982–2010, and of the independent validation for 2011–2018, indicate that this new method provides good predictions of monthly precipitation over NEC. There was good agreement between the observed and predicted monthly precipitation, with correlation coefficients of 0.45, 0.40 and 0.55, and hit rates of 73, 62 and 73% for June, July and August precipitation, respectively, for the period 1982 to 2018.

A hybrid statistical‐dynamical prediction scheme for summer monthly precipitation over Northeast China

“…Compared to statistical method only based on preceding predictors from SST in Ma and Sun (2021), the prediction skill was improved via importing simultaneous CGCM predicted information and MME method, with more sub‐regions covered with significant positive TCC and higher HITs. For regional monthly precipitation, the correlation coefficients are improved from 0.45/0.40/0.55 to 0.71/0.49/0.73, the hit rates are improved from 73%/62%/73% to 77%/66%/77%, the RMSE are decreased from 0.69/0.84/0.83 mm/day to 0.53/0.80/0.79 mm/day for June/July/August.…”

“…The correlation maps between the from step (1) and the preceding winter SST from NOAA_ERSST_V4 were calculated. Preceding predictors from SST were selected by considering the correlation relationship and physical connections, as discussed and listed in Ma and Sun (2021). Select the potential predictors from CGCM predictions.…”

“…Compared to predictions of seasonal mean precipitation, the prediction of monthly precipitation is more complex due to different variability and causal mechanisms over NEC (Chen et al, 2005;Fang et al, 2017Fang et al, , 2018, with correlation coefficient 0.18 and 0.05 among the consecutive months for study period in this study. Recently, Ma and Sun (2021) proposed a statistical prediction scheme for NEC summer monthly prediction based on preceding winter SST signals. This study is an extension of the study of Ma and Sun (2021).…”

“…Recently, Ma and Sun (2021) proposed a statistical prediction scheme for NEC summer monthly prediction based on preceding winter SST signals. This study is an extension of the study of Ma and Sun (2021). We intend to develop a new hybrid prediction scheme combining the advantages of hybrid statistical‐dynamical prediction and multi‐model ensembles to further improve seasonal predictions of summer monthly precipitation over Northeast China.…”

Improving Predictions of Tibetan Plateau Summer Precipitation Using a Sea Surface Temperature Analog-Based Correction Method

“…On the other hand, various statistical or empirical correction methods have been developed in the past decades, aiming to improve model predictions [41][42][43][44][45][46][47][48][49][50][51]. Model prediction errors are flow-dependent, which can vary with changing climate states, and they have been found to be correlated to physical predictors [52].…”