Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin

<table class="table-group" id="tab3"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">RCP</td><td class="align_center">Description</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">RCP4.5</td><td class="align_center">Radiative forcing is stable at 4.5 W/m<sup>2</sup> (∼650 ppm CO<sub>2</sub> eq) by 2100</td></tr><tr><td class="align_left">RCP8.5</td><td class="align_center">Radiative forcing increase to 8.5 W/m<sup>2</sup> (∼1370 ppm CO<sub>2</sub> eq) after 2100</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Overview of RCP4.5 and RCP8.5.</div>

Advances in Meteorology

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Table 3

Table 3: Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin 