Reservoir　computing　has　attracted　considerable　attention　due　to　its　low　training　cost.　However,　existing　neuromorphic　hardware,　focusing　mainly　on　shallow-reservoir　computing,　faces　challenges　in　providing　adequate　spatial　and　temporal　scales　characteristic　for　effective　computing.　Here,　we　report　an　ultra-short　channel　organic　neuromorphic　vertical　transistor　with　distributed　reservoir　states.　The　carrier　dynamics　used　to　map　signals　are　enriched　by　coupled　multivariate　physics　mechanisms,　while　the　vertical　architecture　employed　greatly　increases　the　feedback　intensity　of　the　device.　Consequently,　the　device　as　a　reservoir,　effectively　mapping　sequential　signals　into　distributed　reservoir　state　space　with　1152　reservoir　states,　and　the　range　ratio　of　temporal　and　spatial　characteristics　can　simultaneously　reach　2640　and　650,　respectively.　The　grouped-reservoir　computing　based　on　the　device　can　simultaneously　adapt　to　different　spatiotemporal　task,　achieving　recognition　accuracy　over　94%　and　prediction　correlation　over　95%.　This　work　proposes　a　new　strategy　for　developing　high-performance　reservoir　computing　networks.　Existing　neuromorphic　hardware,　focusing　mainly　on　shallow-reservoir　computing,　is　challenged　in　providing　adequate　spatial　and　temporal　scales　characteristic　for　effective　computing.　Here,　Gao　et　al.　report　an　ultra-short　channel　organic　neuromorphic　vertical　transistor　with　distributed　reservoir　states.