Grape downy mildew is a major biotic constraint to grapevine production worldwide, and its impact is influenced by environmental conditions and varietal susceptibility. Here, we proposed a grape downy mildew model to optimize plant disease management and its application on northern Chinese grapevine areas. A primary and a secondary infection model of P. viticola were integrated to estimate the date of symptoms on set and disease incidence, to give the firSt brEakout and incidEnce of grape Downy Mildew model (SEE_DM). The experimental data for model calibration were collected on two grapevine varietals with high and moderate susceptibility to downy mildew grown in a multiyear (2009, 2011–2019) and multisite (Beijing, Shenyang and Yantai) trial. A model sensitivity analysis (Sobol analysis) drove the selection of the subset of relevant parameters to be adjusted in calibration. The model adequately reproduced the observed downy mildew incidence, obtaining high R2 (0.89), Nash-Sutcliffe modelling efficiency (0.72), and low RMSE (9–16%). The model correctly estimated the disease onset date in all conditions but two (late by 5 and 13 days), demonstrating to be a valid tool to take timely decision to limit the time course of the disease. The model was then projected in three sites under varying temperature and rainfall to analyses their effects on the trends of grape downy mildew incidence, using a variety with moderate susceptibility. This study highlights that Northern China is gradually becoming more suitable for grape downy mildew infections, along with warmer temperatures and more frequent rainfall.