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Abstract Dynamical downscaling with a 20 km horizontal resolution was undertaken over East Asia for the period May–August in 1991–2015 using the Weather Research and Forecasting (WRF) model with Grell-3D ensemble cumulus parameterization as a product of the Impact of Initialized Land Temperature and Snowpack on Sub-Seasonal to Seasonal Prediction (LS4P) program. Simulated climatological precipitation biases were investigated over land during June when heavy precipitation occurred. Simulations underestimated precipitation along the Meiyu/Baiu rainband, while overestimating it farther north. Dry and wet biases expanded to south and north of the Yangtze River in China, respectively, marking years with poor precipitation simulations. Model biases in synoptic-scale circulation patterns indicate a weakened clockwise circulation over the western North Pacific in the model due to active convection there, and suppressed northward moisture transport to the Meiyu/Baiu rainband. Moisture convergence was slightly enhanced over central China due to an apparent anticyclonic circulation bias over northern China. In years with large biases, positive feedback between reduced moisture inflow and inactive convection occurred over southern China, while moisture transport to central China intensified on regional scales, with amplification of dry and wet biases over China. The Kain–Fritch scheme was used to test the influence of cumulus parameterization, improving the dry bias over southern China due to the modification of synoptic-scale circulation patterns in the lower troposphere. However, precipitation was further overestimated over central China, with the accuracy of precipitation distribution deteriorating. 

Abstract. Subseasonal-to-seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging, but also has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges(GEWEX)/Global Atmospheric System Study (GASS) has launched a new initiativecalled “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction” (LS4P) as the first international grass-roots effort to introduce spring land surface temperature(LST)/subsurface temperature (SUBT) anomalies over high mountain areas as acrucial factor that can lead to significant improvement in precipitationprediction through the remote effects of land–atmosphere interactions. LS4P focuses on process understanding and predictability, and hence it is differentfrom, and complements, other international projects that focus on theoperational S2S prediction. More than 40 groups worldwide have participated in this effort, including 21 Earth system models, 9 regionalclimate models, and 7 data groups. This paper provides an overview of the history and objectives of LS4P, provides the first-phase experimental protocol (LS4P-I) which focuses on the remote effect ofthe Tibetan Plateau, discusses the LST/SUBT initialization, and presents thepreliminary results. Multi-model ensemble experiments and analyses ofobservational data have revealed that the hydroclimatic effect of the springLST on the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation beyond EastAsia and its S2S prediction. Preliminary studies and analysis have alsoshown that LS4P models are unable to preserve the initialized LST anomaliesin producing the observed anomalies largely for two main reasons: (i) inadequacies in the land models arising from total soil depths which are tooshallow and the use of simplified parameterizations, which both tend to limit the soil memory; (ii) reanalysis data, which are used for initial conditions, have large discrepancies from the observed mean state andanomalies of LST over the Tibetan Plateau. Innovative approaches have beendeveloped to largely overcome these problems.