Interpolating temperature data on geographical map using python, not giving good results on the edges

I am trying to plot some correlation data about temperature on a stereographic map of the north pole. the data is from different stations in this region.

I am using: from scipy.interpolate : griddata and from mpl_toolkits.basemap: Basemap. My code for the plotting part looks like the following: I am not very happy with how it looks in the south of Greenland. and some parts of Canada and on the edges in general. Can anyone suggest what I can do to improve the map?

The Figure:

import numpy as np
from scipy.interpolate import griddata
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import pandas as pd
from itertools import chain

def draw_map(m, scale=0.7):
    # draw a shaded-relief image
    # m.bluemarble(scale=scale)
    m.shadedrelief()
    
    
    # lats and longs are returned as a dictionary
    lats = m.drawparallels(np.linspace(-90, 90, 13))
    lons = m.drawmeridians(np.linspace(-180, 180, 13))

    # keys contain the plt.Line2D instances
    lat_lines = chain(*(tup[1][0] for tup in lats.items()))
    lon_lines = chain(*(tup[1][0] for tup in lons.items()))
    all_lines = chain(lat_lines, lon_lines)
    
    # cycle through these lines and set the desired style
    for line in all_lines:
        line.set(linestyle='-', alpha=0.3, color='w')

mydata = pd.read_csv("C:/Users/negar/negar/NewDirectory/cor_30.csv")

fig = plt.figure(figsize = (20, 20))
m = Basemap(projection='npstere',boundinglat=55,lon_0=0,resolution='l')
m.drawparallels(np.arange(-80.,81.,10.),  linewidth=0.4)
m.drawmeridians(np.arange(-180.,181.,10.),  linewidth=0.4)
x, y = m(mydata.iloc[:,5], mydata.iloc[:,4])
Jan_Feb = mydata.iloc[:, 6]
plt.scatter(x, y, c = Jan_Feb, cmap = "RdBu", marker = "o")
plt.colorbar(shrink = 0.65)
plt.title("Jan_Feb Correlation")
draw_map(m)

lllon = -180
lllat = 60
urlon = 180
urlat = 90
geo_df = mydata

# Make the edges:
lat = np.array(geo_df.iloc[:,4])
lat_all= np.append(lat, [lllat, urlat]) 
lon = np.array(geo_df.iloc[:,5])
lon_all = np.append(lon, [lllon, urlon])
data = np.array(geo_df.iloc[:,6])
# set up basemap chose projection!
m = Basemap(projection='npstere',boundinglat=55,lon_0=0,resolution='l')

# transform coordinates to map projection m
m_lon, m_lat = m(*(lon, lat))
m_on_all, m_lat_all = m(*(lon_all, lat_all))
# generate grid data
numcols, numrows = 500, 500
xi = np.linspace(m_lon_all.min(), m_lon_all.max(), numcols)
yi = np.linspace(m_lat_all.min(), m_lat_all.max(), numrows)
xi, yi = np.meshgrid(xi, yi)

# interpolate, there are better methods, especially if you have many datapoints
zi = griddata((m_lon,m_lat),data,(xi,yi),method='linear')

fig, ax = plt.subplots(figsize=(12, 12))

# draw map details
m.shadedrelief()
# m.drawmapboundary(fill_color = 'lightgreen', zorder = 1)

# Plot interpolated temperatures
m.contourf(xi, yi, zi, 500, cmap='RdBu', zorder = 2)

m.drawlsmask(ocean_color='skyblue', land_color=(0, 0, 0, 0), lakes=True, zorder = 3)

m.drawparallels(np.arange(-80.,81.,10.),  linewidth=0.4)
m.drawmeridians(np.arange(-180.,181.,10.),  linewidth=0.4)

c=plt.colorbar(shrink=0.75)
plt.clim(-1, 1) 
plt.title('Jan Feb Correlation')

plt.show()