Precipitation　is　an　essential　part　of　the　hydrological　cycle.　Objective　change-point　detection　plays　an　important　role　in　the　research　on　extreme　climate　events　and　risk　assessment　in　the　context　of　global　climate　change.　The　study　can　automatically　identify　and　extract　multiple　change　points　over　a　lengthy　time　series　and　calculate　trends　over　several　segmentations　by　uniting　the　maximum　likelihood　approach　and　the　nonparametric　Mann-Kendall　trend　test　which　is　also　compared　with　ordinary　least　squares　(OLS).　The　observed　station　precipitation　data　were　compiled　over　Hebei　Province　in　China　from　1961　to　2014.　Temporal-spatial　characteristics　were　also　investigated　by　using　several　indices:　the　number　of　change　points,　standard　deviation,　timing　of　change　points,　minimum　and　maximum　trends　for　segmentations,　and　the　generalization　trend.　Obvious　change　points　were　generally　around　1974,　1981,　and　1998,　and　occurred　at　few　stations　before　1974　and　after　2000,　indicating　that　precipitation　was　relatively　stable　in　the　study　area　during　the　periods　1961-1974　and　2000-2014;　the　minimum　trend　for　segmentations　decreased　over　all　stations;　the　maximum　trend　for　segmentations　increased　over　all　stations　except　Leting;　and　generalization　trend　weakened　abrupt　changes　in　specific　time　sections　among　the　multiple　segmentations.　Change-point　detection　followed　by　trend　analysis　can　detect　an　obvious　increasing　or　decreasing　trend　over　certain　parts　of　the　time　series　and　the　proposed　method　can　serve　as　a　management　tool　with　proper　measures　to　deal　with　climate　change.　The　results　for　both　segmentations　and　generalization　can　provide　a　workable　reference　for　managing　regional　water　resources　and　implementing　strategies　to　mitigate　meteorological　risks.